obsutils.py 10.4 KB

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# ==========================================================================
# This script provides a number of functions that are useful for handling
# CTA observations.
#
# Copyright (C) 2011-2014 Juergen Knoedlseder
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
# ==========================================================================
import ctools
import gammalib


# ===================== #
# Simulate observations #
# ===================== #
def sim(obs, log=False, debug=False, seed=0, nbins=0, binsz=0.05, npix=200):
    """
    Simulate events for all observations in the container.
    
    Parameters:
     obs   - Observation container
    Keywords:
     log   - Create log file(s)
     debug - Create screen dump
     seed  - Seed value for simulations (default: 0)
     nbins - Number of energy bins (default: 0=unbinned)
     binsz - Pixel size for binned simulation (deg/pixel)
     npix  - Number of pixels in X and Y for binned simulation
    """
    # Allocate ctobssim application and set parameters
    sim = ctools.ctobssim(obs)
    sim['seed'].integer(seed)

    # Optionally open the log file
    if log:
        sim.logFileOpen()

    # Optionally switch-on debugging model
    if debug:
        sim["debug"].boolean(True)

    # Run ctobssim application. This will loop over all observations in the
    # container and simulation the events for each observation. Note that
    # events are not added together, they still apply to each observation
    # separately.
    sim.run()

    # Binned option?
    if nbins > 0:

        # Determine common energy boundaries for observations
        emin = None
        emax = None
        for run in sim.obs():
            run_emin = run.events().ebounds().emin().TeV()
            run_emax = run.events().ebounds().emax().TeV()
            if emin == None:
                emin = run_emin
            elif run_emin > emin:
                emin = run_emin
            if emax == None:
                emax = run_emax
            elif run_emax > emax:
                emax = run_emax

        # Allocate ctbin application and set parameters
        bin = ctools.ctbin(sim.obs())
        bin["ebinalg"].string("LOG")
        bin["emin"].real(emin)
        bin["emax"].real(emax)
        bin["enumbins"].integer(nbins)
        bin["usepnt"].boolean(True) # Use pointing for map centre
        bin["nxpix"].integer(npix)
        bin["nypix"].integer(npix)
        bin["binsz"].real(binsz)
        bin["coordsys"].string("GAL")
        bin["proj"].string("TAN")

        # Optionally open the log file
        if log:
            bin.logFileOpen()

        # Optionally switch-on debugging model
        if debug:
            bin["debug"].boolean(True)

        # Run ctbin application. This will loop over all observations in
        # the container and bin the events in counts maps
        bin.run()

        # Make a deep copy of the observation that will be returned
        # (the ctbin object will go out of scope one the function is
        # left)
        obs = bin.obs().copy()

    else:

        # Make a deep copy of the observation that will be returned
        # (the ctobssim object will go out of scope one the function is
        # left)
        obs = sim.obs().copy()

    # Delete the simulation
    del sim

    # Return observation container
    return obs


# ================ #
# Fit observations #
# ================ #
def fit(obs, log=False, debug=False):
    """
    Perform maximum likelihood fitting of observations in the container.
    
    Parameters:
     obs   - Observation container
    Keywords:
     log   - Create log file(s)
     debug - Create screen dump
    """
    # Allocate ctlike application
    like = ctools.ctlike(obs)

    # Optionally open the log file
    if log:
        like.logFileOpen()

    # Optionally switch-on debugging model
    if debug:
        like["debug"].boolean(True)

    # Run ctlike application.
    like.run()

    # Return observations
    return like


# ================= #
# Create counts map #
# ================= #
def cntmap(obs, proj="TAN", coord="GAL", xval=0.0, yval=0.0, \
           binsz=0.05, nxpix=200, nypix=200, \
           outname="cntmap.fits"):
    """
    Creates a counts map by combining the events of all observations.
    The counts map will be a summed map over all energies.
    
    Parameters:
     obs     - Observation container
    Keywords:
     proj    - Projection type (e.g. TAN, CAR, STG, ...) (default: TAN)
     coord   - Coordinate type (GAL, CEL) (default: GAL)
     xval    - Reference longitude value [deg] (default: 0.0)
     yval    - Reference latitude value [deg] (default: 0.0)
     binsz   - Pixel size [deg/pixel] (default: 0.05)
     nxpix   - Number of pixels in X direction (default: 200)
     nypix   - Number of pixels in Y direction (default: 200)
     outname - Counts map FITS filename (default: cntmap.fits)
    """
    # Allocate counts map
    map = gammalib.GSkymap(proj, coord, xval, yval, -binsz, binsz, nxpix, nypix, 1)

    # Fill all observations
    for run in obs:

        # Loop over all events
        for event in run.events():

            # Determine sky pixel
            skydir = gammalib.GCTAInstDir(event.dir()).dir()
            pixel  = map.dir2inx(skydir)

            # Set pixel
            map[pixel] += 1.0

    # Save sky map. The clobber flag is set to True, so any existing FITS
    # file will be overwritten.
    map.save(outname, True)

    # Return counts map
    return map


# ================ #
# Create model map #
# ================ #
def modmap(obs, eref=0.1, proj="TAN", coord="GAL", xval=0.0, yval=0.0, \
           binsz=0.05, nxpix=200, nypix=200, \
           outname="modmap.fits"):
    """
    Make model map for a given reference energy by combining all observations.
    The model map will be evaluated for a given reference energy 'eref' and will
    be given in units of [counts/(sr MeV s)].
    
    Parameters:
     obs     - Observation container
    Keywords:
     eref    - Reference energy for which model is created [TeV] (default: 0.1)
     proj    - Projection type (e.g. TAN, CAR, STG, ...) (default: TAN)
     coord   - Coordinate type (GAL, CEL) (default: GAL)
     xval    - Reference longitude value [deg] (default: 0.0)
     yval    - Reference latitude value [deg] (default: 0.0)
     binsz   - Pixel size [deg/pixel] (default: 0.05)
     nxpix   - Number of pixels in X direction (default: 200)
     nypix   - Number of pixels in Y direction (default: 200)
     outname - Model map FITS filename (default: modmap.fits)
    """
    # Allocate model map
    map = gammalib.GSkymap(proj, coord, xval, yval, -binsz, binsz, nxpix, nypix, 1)

    # Set reference energy, time and direction. The time is not initialised and is
    # in fact not used (as the IRF is assumed to be time independent for now).
    # The sky direction is set later using the pixel values.
    energy  = gammalib.GEnergy()
    time    = gammalib.GTime()
    instdir = gammalib.GCTAInstDir()
    energy.TeV(eref)

    # Loop over all map pixels
    for pixel in range(map.npix()):

        # Get sky direction
        skydir = map.inx2dir(pixel)
        instdir.dir(skydir)

        # Create event atom for map pixel
        atom = gammalib.GCTAEventAtom()
        atom.dir(instdir)
        atom.energy(energy)
        atom.time(time)

        # Initialise model value
        value = 0.0

        # Loop over all observations
        for run in obs:
            value += obs.models().eval(atom, run)

        # Set map value
        map[pixel] = value

    # Save sky map
    map.save(outname, True)

    # Return model map
    return map


# ======================= #
# Set one CTA observation #
# ======================= #
def set(pntdir, tstart=0.0, duration=1800.0, deadc=0.95, \
        emin=0.1, emax=100.0, rad=5.0, \
        irf="cta_dummy_irf", caldb=""):
    """
    Returns a single CTA observation. By looping over this function we can
    add CTA observations to the observation container.
    
    Parameters:
     pntdir   - Pointing direction
    Keywords:
     tstart   - Start time [seconds] (default: 0.0)
     duration - Duration of observation [seconds] (default: 1800.0)
     deadc    - Deadtime correction factor (default: 0.95)
     emin     - Minimum event energy [TeV] (default: 0.1)
     emax     - Maximum event energy [TeV] (default: 100.0)
     rad      - ROI radius used for analysis [deg] (default: 5.0)
     irf      - Instrument response function (default: cta_dummy_irf)
     caldb    - Calibration database path (default: "")
    """
    # Allocate CTA observation
    obs = gammalib.GCTAObservation()

    # Set calibration database
    db = gammalib.GCaldb()
    if (gammalib.dir_exists(caldb)):
        db.dir(caldb)
    else:
        db.open("cta", caldb)

    # Set pointing direction
    pnt = gammalib.GCTAPointing()
    pnt.dir(pntdir)
    obs.pointing(pnt)

    # Set ROI
    roi     = gammalib.GCTARoi()
    instdir = gammalib.GCTAInstDir()
    instdir.dir(pntdir)
    roi.centre(instdir)
    roi.radius(rad)

    # Set GTI
    gti   = gammalib.GGti()
    start = gammalib.GTime(tstart)
    stop  = gammalib.GTime(tstart+duration)
    gti.append(start, stop)

    # Set energy boundaries
    ebounds = gammalib.GEbounds()
    e_min   = gammalib.GEnergy()
    e_max   = gammalib.GEnergy()
    e_min.TeV(emin)
    e_max.TeV(emax)
    ebounds.append(e_min, e_max)

    # Allocate event list
    events = gammalib.GCTAEventList()
    events.roi(roi)
    events.gti(gti)
    events.ebounds(ebounds)
    obs.events(events)

    # Set instrument response
    obs.response(irf, db)

    # Set ontime, livetime, and deadtime correction factor
    obs.ontime(duration)
    obs.livetime(duration*deadc)
    obs.deadc(deadc)

    # Return observation
    return obs