Source code for

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import collections
import logging
import numpy as np
from astropy.coordinates import AltAz, Angle, SkyCoord
from astropy.coordinates.angle_utilities import angular_separation
from astropy.table import Table
from astropy.table import vstack as vstack_tables
from astropy.units import Quantity, Unit
from gammapy.maps import MapAxis, MapCoord, RegionGeom, WcsNDMap
from gammapy.utils.fits import earth_location_from_dict
from gammapy.utils.scripts import make_path
from gammapy.utils.testing import Checker
from gammapy.utils.time import time_ref_from_dict

__all__ = ["EventList"]

log = logging.getLogger(__name__)

[docs]class EventList: """Event list. Event list data is stored in ``table`` (`~astropy.table.Table`) data member. The most important reconstructed event parameters are available as the following columns: - ``TIME`` - Mission elapsed time (sec) - ``RA``, ``DEC`` - ICRS system position (deg) - ``ENERGY`` - Energy (usually MeV for Fermi and TeV for IACTs) Note that ``TIME`` is usually sorted, but sometimes it is not. E.g. when simulating data, or processing it in certain ways. So generally any analysis code should assume ``TIME`` is not sorted. Other optional (columns) that are sometimes useful for high-level analysis: - ``GLON``, ``GLAT`` - Galactic coordinates (deg) - ``DETX``, ``DETY`` - Field of view coordinates (deg) Note that when reading data for analysis you shouldn't use those values directly, but access them via properties which create objects of the appropriate class: - `time` for ``TIME`` - `radec` for ``RA``, ``DEC`` - `energy` for ``ENERGY`` - `galactic` for ``GLON``, ``GLAT`` Parameters ---------- table : `~astropy.table.Table` Event list table """ def __init__(self, table): self.table = table
[docs] @classmethod def read(cls, filename, **kwargs): """Read from FITS file. Format specification: :ref:`gadf:iact-events` Parameters ---------- filename : `pathlib.Path`, str Filename """ filename = make_path(filename) kwargs.setdefault("hdu", "EVENTS") table =, **kwargs) return cls(table=table)
[docs] @classmethod def from_stack(cls, event_lists, **kwargs): """Stack (concatenate) list of event lists. Calls `~astropy.table.vstack`. Parameters ---------- event_lists : list list of `` to stack """ tables = [_.table for _ in event_lists] stacked_table = vstack_tables(tables, **kwargs) return cls(stacked_table)
[docs] def stack(self, other): """Stack with another EventList in place. Calls `~astropy.table.vstack`. Parameters ---------- other : `` Event list to stack to self """ self.table = vstack_tables([self.table, other.table])
def __str__(self): info = self.__class__.__name__ + "\n" info += "-" * len(self.__class__.__name__) + "\n\n" instrument = self.table.meta.get("INSTRUME") info += f"\tInstrument : {instrument}\n" telescope = self.table.meta.get("TELESCOP") info += f"\tTelescope : {telescope}\n" obs_id = self.table.meta.get("OBS_ID", "") info += f"\tObs. ID : {obs_id}\n\n" info += f"\tNumber of events : {len(self.table)}\n" rate = len(self.table) / self.observation_time_duration info += f"\tEvent rate : {rate:.3f}\n\n" info += f"\tTime start : {self.observation_time_start}\n" info += f"\tTime stop : {self.observation_time_stop}\n\n" info += f"\tMin. energy : {np.min(}\n" info += f"\tMax. energy : {np.max(}\n" info += f"\tMedian energy : {np.median(}\n\n" if self.is_pointed_observation: offset_max = np.max(self.offset) info += f"\tMax. offset : {offset_max:.1f}\n" return info.expandtabs(tabsize=2) @property def time_ref(self): """Time reference (`~astropy.time.Time`).""" return time_ref_from_dict(self.table.meta) @property def time(self): """Event times (`~astropy.time.Time`). Notes ----- Times are automatically converted to 64-bit floats. With 32-bit floats times will be incorrect by a few seconds when e.g. adding them to the reference time. """ met = Quantity(self.table["TIME"].astype("float64"), "second") return self.time_ref + met @property def observation_time_start(self): """Observation start time (`~astropy.time.Time`).""" return self.time_ref + Quantity(self.table.meta["TSTART"], "second") @property def observation_time_stop(self): """Observation stop time (`~astropy.time.Time`).""" return self.time_ref + Quantity(self.table.meta["TSTOP"], "second") @property def radec(self): """Event RA / DEC sky coordinates (`~astropy.coordinates.SkyCoord`).""" lon, lat = self.table["RA"], self.table["DEC"] return SkyCoord(lon, lat, unit="deg", frame="icrs") @property def galactic(self): """Event Galactic sky coordinates (`~astropy.coordinates.SkyCoord`). Always computed from RA / DEC using Astropy. """ return self.radec.galactic @property def energy(self): """Event energies (`~astropy.units.Quantity`).""" return self.table["ENERGY"].quantity @property def galactic_median(self): """Median position in radec""" galactic = self.galactic median_lon = np.median(galactic.l.wrap_at("180d")) median_lat = np.median(galactic.b) return SkyCoord(median_lon, median_lat, frame="galactic")
[docs] def select_row_subset(self, row_specifier): """Select table row subset. Parameters ---------- row_specifier : slice, int, or array of ints Specification for rows to select, passed on to ``self.table[row_specifier]``. Returns ------- event_list : `EventList` New event list with table row subset selected Examples -------- Use a boolean mask as ``row_specifier``: mask = events.table['FOO'] > 42 events2 = events.select_row_subset(mask) Use row index array as ``row_specifier``: idx = np.where(events.table['FOO'] > 42)[0] events2 = events.select_row_subset(idx) """ table = self.table[row_specifier] return self.__class__(table=table)
[docs] def select_energy(self, energy_range): """Select events in energy band. Parameters ---------- energy_range : `~astropy.units.Quantity` Energy range ``[energy_min, energy_max)`` Returns ------- event_list : `EventList` Copy of event list with selection applied. Examples -------- >>> from astropy.units import Quantity >>> from import EventList >>> event_list ='events.fits') >>> energy_range = Quantity([1, 20], 'TeV') >>> event_list = event_list.select_energy() """ energy = mask = energy_range[0] <= energy mask &= energy < energy_range[1] return self.select_row_subset(mask)
[docs] def select_time(self, time_interval): """Select events in time interval. Parameters ---------- time_interval : `astropy.time.Time` Start time (inclusive) and stop time (exclusive) for the selection. Returns ------- events : `EventList` Copy of event list with selection applied. """ time = self.time mask = time_interval[0] <= time mask &= time < time_interval[1] return self.select_row_subset(mask)
[docs] def select_region(self, region, wcs=None): """Select events in given region. Parameters ---------- region : `~regions.SkyRegion` or str Sky region or string defining a sky region wcs : `~astropy.wcs.WCS` World coordinate system transformation Returns ------- event_list : `EventList` Copy of event list with selection applied. """ geom = RegionGeom.create(region, wcs=wcs) mask = geom.contains(self.radec) return self.select_row_subset(mask)
[docs] def select_parameter(self, parameter, band): """Select events with respect to a specified parameter. Parameters ---------- parameter : str Parameter used for the selection. Must be present in `self.table`. band : tuple or `astropy.units.Quantity` Min and max value for the parameter to be selected (min <= parameter < max). If parameter is not dimensionless you have to provide a Quantity. Returns ------- event_list : `EventList` Copy of event list with selection applied. Examples -------- >>> from import EventList >>> event_list ='events.fits') >>> phase_region = (0.3, 0.5) >>> event_list = event_list.select_parameter(parameter='PHASE', band=phase_region) """ mask = band[0] <= self.table[parameter].quantity mask &= self.table[parameter].quantity < band[1] return self.select_row_subset(mask)
def _default_plot_energy_edges(self): energy = return MapAxis.from_energy_bounds(energy.min(), energy.max(), 50).edges
[docs] def plot_energy(self, ax=None, energy_edges=None, **kwargs): """Plot counts as a function of energy.""" import matplotlib.pyplot as plt ax = plt.gca() if ax is None else ax kwargs.setdefault("log", True) kwargs.setdefault("histtype", "step") if energy_edges is None: energy_edges = self._default_plot_energy_edges() unit = energy_edges.unit ax.hist(, bins=energy_edges.value, **kwargs) ax.loglog() ax.set_xlabel(f"Energy ({unit})") ax.set_ylabel("Counts") return ax
[docs] def plot_time(self, ax=None): """Plots an event rate time curve. Parameters ---------- ax : `~matplotlib.axes.Axes` or None Axes Returns ------- ax : `~matplotlib.axes.Axes` Axes """ import matplotlib.pyplot as plt ax = plt.gca() if ax is None else ax # Note the events are not necessarily in time order time = self.table["TIME"] time = time - np.min(time) ax.set_xlabel("Time (sec)") ax.set_ylabel("Counts") y, x_edges = np.histogram(time, bins=20) xerr = np.diff(x_edges) / 2 x = x_edges[:-1] + xerr yerr = np.sqrt(y) ax.errorbar(x=x, y=y, xerr=xerr, yerr=yerr, fmt="none") return ax
[docs] def plot_offset2_distribution(self, ax=None, center=None, **kwargs): """Plot offset^2 distribution of the events. The distribution shown in this plot is for this quantity:: offset = center.separation(events.radec).deg offset2 = offset ** 2 Note that this method is just for a quicklook plot. If you want to do computations with the offset or offset^2 values, you can use the line above. As an example, here's how to compute the 68% event containment radius using `numpy.percentile`:: import numpy as np r68 = np.percentile(offset, q=68) Parameters ---------- ax : `~matplotlib.axes.Axes` (optional) Axes center : `astropy.coordinates.SkyCoord` Center position for the offset^2 distribution. Default is the observation pointing position. **kwargs : Extra keyword arguments are passed to `matplotlib.pyplot.hist`. Returns ------- ax : `~matplotlib.axes.Axes` Axes Examples -------- Load an example event list: >>> from import EventList >>> events ='$GAMMAPY_DATA/hess-dl3-dr1/data/hess_dl3_dr1_obs_id_023523.fits.gz') Plot the offset^2 distribution wrt. the observation pointing position (this is a commonly used plot to check the background spatial distribution): >>> events.plot_offset2_distribution() Plot the offset^2 distribution wrt. the Crab pulsar position (this is commonly used to check both the gamma-ray signal and the background spatial distribution): >>> import numpy as np >>> from astropy.coordinates import SkyCoord >>> center = SkyCoord(83.63307, 22.01449, unit='deg') >>> bins = np.linspace(start=0, stop=0.3 ** 2, num=30) >>> events.plot_offset2_distribution(center=center, bins=bins) Note how we passed the ``bins`` option of `matplotlib.pyplot.hist` to control the histogram binning, in this case 30 bins ranging from 0 to (0.3 deg)^2. """ import matplotlib.pyplot as plt ax = plt.gca() if ax is None else ax if center is None: center = self._plot_center offset2 = center.separation(self.radec).deg ** 2 kwargs.setdefault("histtype", "step") kwargs.setdefault("bins", 30) ax.hist(offset2, **kwargs) ax.set_xlabel("Offset^2 (deg^2)") ax.set_ylabel("Counts") return ax
[docs] def plot_energy_offset(self, ax=None, center=None): """Plot counts histogram with energy and offset axes.""" import matplotlib.pyplot as plt from matplotlib.colors import LogNorm ax = plt.gca() if ax is None else ax if center is None: center = self._plot_center energy_bounds = self._default_plot_energy_edges().to_value( offset = center.separation(self.radec) offset_max = offset.max() offset_bounds = np.linspace(0, offset_max.deg, 30) counts = np.histogram2d(, y=offset.deg, bins=(energy_bounds, offset_bounds), )[0] ax.pcolormesh(energy_bounds, offset_bounds, counts.T, norm=LogNorm()) ax.set_xscale("log") ax.set_xlabel(f"Energy ({})") ax.set_ylabel(f"Offset ({offset.unit})")
[docs] def check(self, checks="all"): """Run checks. This is a generator that yields a list of dicts. """ checker = EventListChecker(self) return
[docs] def map_coord(self, geom): """Event map coordinates for a given geometry. Parameters ---------- geom : `~gammapy.maps.Geom` Geometry Returns ------- coord : `~gammapy.maps.MapCoord` Coordinates """ coord = {"skycoord": self.radec} cols = {k.upper(): v for k, v in self.table.columns.items()} for axis in geom.axes: try: col = cols[] coord[] = Quantity(col).to(axis.unit) except KeyError: raise KeyError(f"Column not found in event list: {!r}") return MapCoord.create(coord)
[docs] def select_map_mask(self, mask): """Select events inside a mask (`EventList`). Parameters ---------- mask : `~gammapy.maps.Map` Mask """ coord = self.map_coord(mask.geom) values = mask.get_by_coord(coord) valid = values > 0 return self.select_row_subset(valid)
@property def observatory_earth_location(self): """Observatory location (`~astropy.coordinates.EarthLocation`).""" return earth_location_from_dict(self.table.meta) @property def observation_time_duration(self): """Observation time duration in seconds (`~astropy.units.Quantity`). This is a keyword related to IACTs The wall time, including dead-time. """ time_delta = (self.observation_time_stop - self.observation_time_start).sec return Quantity(time_delta, "s") @property def observation_live_time_duration(self): """Live-time duration in seconds (`~astropy.units.Quantity`). The dead-time-corrected observation time. - In Fermi-LAT it is automatically provided in the header of the event list. - In IACTs is computed as ``t_live = t_observation * (1 - f_dead)`` where ``f_dead`` is the dead-time fraction. """ return Quantity(self.table.meta["LIVETIME"], "second") @property def observation_dead_time_fraction(self): """Dead-time fraction (float). This is a keyword related to IACTs Defined as dead-time over observation time. Dead-time is defined as the time during the observation where the detector didn't record events: The dead-time fraction is used in the live-time computation, which in turn is used in the exposure and flux computation. """ return 1 - self.table.meta["DEADC"] @property def altaz_frame(self): """ALT / AZ frame (`~astropy.coordinates.AltAz`).""" return AltAz(obstime=self.time, location=self.observatory_earth_location) @property def altaz(self): """ALT / AZ position computed from RA / DEC (`~astropy.coordinates.SkyCoord`).""" return self.radec.transform_to(self.altaz_frame) @property def altaz_from_table(self): """ALT / AZ position from table (`~astropy.coordinates.SkyCoord`).""" lon = self.table["AZ"] lat = self.table["ALT"] return SkyCoord(lon, lat, unit="deg", frame=self.altaz_frame) @property def pointing_radec(self): """Pointing RA / DEC sky coordinates (`~astropy.coordinates.SkyCoord`).""" info = self.table.meta lon, lat = info["RA_PNT"], info["DEC_PNT"] return SkyCoord(lon, lat, unit="deg", frame="icrs") @property def offset(self): """Event offset from the array pointing position (`~astropy.coordinates.Angle`).""" position = self.radec center = self.pointing_radec offset = center.separation(position) return Angle(offset, unit="deg") @property def offset_from_median(self): """Event offset from the median position (`~astropy.coordinates.Angle`).""" position = self.radec center = self.galactic_median offset = center.separation(position) return Angle(offset, unit="deg")
[docs] def select_offset(self, offset_band): """Select events in offset band. Parameters ---------- offset_band : `~astropy.coordinates.Angle` offset band ``[offset_min, offset_max)`` Returns ------- event_list : `EventList` Copy of event list with selection applied. """ offset = self.offset mask = offset_band[0] <= offset mask &= offset < offset_band[1] return self.select_row_subset(mask)
@property def is_pointed_observation(self): """Whether observation is pointed""" return "RA_PNT" in self.table.meta
[docs] def peek(self, allsky=False): """Quick look plots. Parameters ---------- allsky : bool Wheter to look at the events allsky """ import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec if allsky: gs = gridspec.GridSpec(nrows=2, ncols=2) fig = plt.figure(figsize=(8, 8)) else: gs = gridspec.GridSpec(nrows=2, ncols=3) fig = plt.figure(figsize=(12, 8)) # energy plot ax_energy = fig.add_subplot(gs[1, 0]) self.plot_energy(ax=ax_energy) # offset plots if not allsky: ax_offset = fig.add_subplot(gs[0, 1]) self.plot_offset2_distribution(ax=ax_offset) ax_energy_offset = fig.add_subplot(gs[0, 2]) self.plot_energy_offset(ax=ax_energy_offset) # time plot ax_time = fig.add_subplot(gs[1, 1]) self.plot_time(ax=ax_time) # image plot m = self._counts_image(allsky=allsky) if allsky: ax_image = fig.add_subplot(gs[0, :], projection=m.geom.wcs) else: ax_image = fig.add_subplot(gs[0, 0], projection=m.geom.wcs) m.plot(ax=ax_image, stretch="sqrt", vmin=0) plt.subplots_adjust(wspace=0.3)
@property def _plot_center(self): if self.is_pointed_observation: return self.pointing_radec else: return self.galactic_median @property def _plot_width(self): if self.is_pointed_observation: offset = self.offset else: offset = self.offset_from_median return 2 * offset.max() def _counts_image(self, allsky): if allsky: opts = { "npix": (360, 180), "binsz": 1.0, "proj": "AIT", "frame": "galactic", } else: opts = { "width": self._plot_width, "binsz": 0.05, "proj": "TAN", "frame": "galactic", "skydir": self._plot_center, } m = WcsNDMap.create(**opts) m.fill_by_coord(self.radec) m = m.smooth(width=0.5) return m
[docs] def plot_image(self, allsky=False): """Quick look counts map sky plot.""" m = self._counts_image(allsky=allsky) m.plot(stretch="sqrt")
class EventListChecker(Checker): """Event list checker. Data format specification: ref:`gadf:iact-events` Parameters ---------- event_list : `` Event list """ CHECKS = { "meta": "check_meta", "columns": "check_columns", "times": "check_times", "coordinates_galactic": "check_coordinates_galactic", "coordinates_altaz": "check_coordinates_altaz", } accuracy = {"angle": Angle("1 arcsec"), "time": Quantity(1, "microsecond")} # meta_required = [ "HDUCLASS", "HDUDOC", "HDUVERS", "HDUCLAS1", "OBS_ID", "TSTART", "TSTOP", "ONTIME", "LIVETIME", "DEADC", "RA_PNT", "DEC_PNT", # TODO: what to do about these? # They are currently listed as required in the spec, # but I think we should just require ICRS and those # are irrelevant, should not be used. # 'RADECSYS', # 'EQUINOX', "ORIGIN", "TELESCOP", "INSTRUME", "CREATOR", # "MJDREFI", "MJDREFF", "TIMEUNIT", "TIMESYS", "TIMEREF", # "GEOLON", "GEOLAT", "ALTITUDE", ] _col = collections.namedtuple("col", ["name", "unit"]) columns_required = [ _col(name="EVENT_ID", unit=""), _col(name="TIME", unit="s"), _col(name="RA", unit="deg"), _col(name="DEC", unit="deg"), _col(name="ENERGY", unit="TeV"), ] def __init__(self, event_list): self.event_list = event_list def _record(self, level="info", msg=None): obs_id = self.event_list.table.meta["OBS_ID"] return {"level": level, "obs_id": obs_id, "msg": msg} def check_meta(self): meta_missing = sorted(set(self.meta_required) - set(self.event_list.table.meta)) if meta_missing: yield self._record( level="error", msg=f"Missing meta keys: {meta_missing!r}" ) def check_columns(self): t = self.event_list.table if len(t) == 0: yield self._record(level="error", msg="Events table has zero rows") for name, unit in self.columns_required: if name not in t.colnames: yield self._record(level="error", msg=f"Missing table column: {name!r}") else: if Unit(unit) != (t[name].unit or ""): yield self._record( level="error", msg=f"Invalid unit for column: {name!r}" ) def check_times(self): dt = (self.event_list.time - self.event_list.observation_time_start).sec if dt.min() < self.accuracy["time"].to_value("s"): yield self._record(level="error", msg="Event times before obs start time") dt = (self.event_list.time - self.event_list.observation_time_stop).sec if dt.max() > self.accuracy["time"].to_value("s"): yield self._record(level="error", msg="Event times after the obs end time") if np.min(np.diff(dt)) <= 0: yield self._record(level="error", msg="Events are not time-ordered.") def check_coordinates_galactic(self): """Check if RA / DEC matches GLON / GLAT.""" t = self.event_list.table if "GLON" not in t.colnames: return galactic = SkyCoord(t["GLON"], t["GLAT"], unit="deg", frame="galactic") separation = self.event_list.radec.separation(galactic).to("arcsec") if separation.max() > self.accuracy["angle"]: yield self._record( level="error", msg="GLON / GLAT not consistent with RA / DEC" ) def check_coordinates_altaz(self): """Check if ALT / AZ matches RA / DEC.""" t = self.event_list.table if "AZ" not in t.colnames: return altaz_astropy = self.event_list.altaz separation = angular_separation(,, t["AZ"].quantity, t["ALT"].quantity, ) if separation.max() > self.accuracy["angle"]: yield self._record( level="error", msg="ALT / AZ not consistent with RA / DEC" )