"""
Classes for generating lists of photons and detected events

The SciPy Proceeding that describes this module in detail may be found at:

http://conference.scipy.org/proceedings/scipy2014/zuhone.html

The algorithms used here are based off of the method used by the
PHOX code (http://www.mpa-garching.mpg.de/~kdolag/Phox/),
developed by Veronica Biffi and Klaus Dolag. References for
PHOX may be found at:

Biffi, V., Dolag, K., Bohringer, H., & Lemson, G. 2012, MNRAS, 420, 3545
http://adsabs.harvard.edu/abs/2012MNRAS.420.3545B

Biffi, V., Dolag, K., Bohringer, H. 2013, MNRAS, 428, 1395
http://adsabs.harvard.edu/abs/2013MNRAS.428.1395B
"""

#-----------------------------------------------------------------------------
# Copyright (c) 2013, yt Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
from yt.extern.six import string_types
from collections import defaultdict
import numpy as np
from yt.funcs import mylog, get_pbar, iterable, ensure_list
from yt.utilities.physical_constants import clight
from yt.utilities.cosmology import Cosmology
from yt.utilities.orientation import Orientation
from yt.visualization.fits_image import assert_same_wcs
from yt.utilities.parallel_tools.parallel_analysis_interface import \
    communication_system, parallel_root_only, get_mpi_type, \
    parallel_capable
from yt.units.yt_array import YTQuantity, YTArray, uconcatenate
from yt.utilities.on_demand_imports import _h5py as h5py
from yt.utilities.on_demand_imports import _astropy
import warnings
import os

comm = communication_system.communicators[-1]

axes_lookup = {"x":("y","z"),
               "y":("z","x"),
               "z":("x","y")}

def force_unicode(value):
    if hasattr(value, 'decode'):
        return value.decode('utf8')
    else:
        return value

def parse_value(value, default_units):
    if isinstance(value, YTQuantity):
        return value.in_units(default_units)
    elif iterable(value):
        return YTQuantity(value[0], value[1]).in_units(default_units)
    else:
        return YTQuantity(value, default_units)

def validate_parameters(first, second, skip=[]):
    keys1 = list(first.keys())
    keys2 = list(first.keys())
    keys1.sort()
    keys2.sort()
    if keys1 != keys2:
        raise RuntimeError("The two inputs do not have the same parameters!")
    for k1, k2 in zip(keys1, keys2):
        if k1 not in skip:
            v1 = first[k1]
            v2 = second[k2]
            if isinstance(v1, string_types) or isinstance(v2, string_types):
                check_equal = v1 == v2
            else:
                check_equal = np.allclose(v1, v2, rtol=0.0, atol=1.0e-10)
            if not check_equal:
                raise RuntimeError("The values for the parameter '%s' in the two inputs" % k1 +
                                   " are not identical (%s vs. %s)!" % (v1, v2))

class PhotonList(object):

    def __init__(self, photons, parameters, cosmo, p_bins):
        self.photons = photons
        self.parameters = parameters
        self.cosmo = cosmo
        self.p_bins = p_bins
        self.num_cells = len(photons["x"])

    def keys(self):
        return self.photons.keys()

    def items(self):
        ret = []
        for k, v in self.photons.items():
            if k == "Energy":
                ret.append((k, self[k]))
            else:
                ret.append((k,v))
        return ret

    def values(self):
        ret = []
        for k, v in self.photons.items():
            if k == "Energy":
                ret.append(self[k])
            else:
                ret.append(v)
        return ret

    def __getitem__(self, key):
        if key == "Energy":
            return [self.photons["Energy"][self.p_bins[i]:self.p_bins[i+1]]
                    for i in range(self.num_cells)]
        else:
            return self.photons[key]

    def __contains__(self, key):
        return key in self.photons

    def __repr__(self):
        return self.photons.__repr__()

    @classmethod
    def from_file(cls, filename):
        r"""
        Initialize a PhotonList from the HDF5 file *filename*.
        """

        photons = {}
        parameters = {}

        f = h5py.File(filename, "r")

        p = f["/parameters"]
        parameters["FiducialExposureTime"] = YTQuantity(p["fid_exp_time"].value, "s")
        parameters["FiducialArea"] = YTQuantity(p["fid_area"].value, "cm**2")
        parameters["FiducialRedshift"] = p["fid_redshift"].value
        parameters["FiducialAngularDiameterDistance"] = YTQuantity(p["fid_d_a"].value, "Mpc")
        parameters["Dimension"] = p["dimension"].value
        parameters["Width"] = YTQuantity(p["width"].value, "kpc")
        parameters["HubbleConstant"] = p["hubble"].value
        parameters["OmegaMatter"] = p["omega_matter"].value
        parameters["OmegaLambda"] = p["omega_lambda"].value

        d = f["/data"]

        num_cells = d["x"][:].shape[0]
        start_c = comm.rank*num_cells//comm.size
        end_c = (comm.rank+1)*num_cells//comm.size

        photons["x"] = YTArray(d["x"][start_c:end_c], "kpc")
        photons["y"] = YTArray(d["y"][start_c:end_c], "kpc")
        photons["z"] = YTArray(d["z"][start_c:end_c], "kpc")
        photons["dx"] = YTArray(d["dx"][start_c:end_c], "kpc")
        photons["vx"] = YTArray(d["vx"][start_c:end_c], "km/s")
        photons["vy"] = YTArray(d["vy"][start_c:end_c], "km/s")
        photons["vz"] = YTArray(d["vz"][start_c:end_c], "km/s")

        n_ph = d["num_photons"][:]

        if comm.rank == 0:
            start_e = np.uint64(0)
        else:
            start_e = n_ph[:start_c].sum()
        end_e = start_e + np.uint64(n_ph[start_c:end_c].sum())

        photons["NumberOfPhotons"] = n_ph[start_c:end_c]

        p_bins = np.cumsum(photons["NumberOfPhotons"])
        p_bins = np.insert(p_bins, 0, [np.uint64(0)])

        photons["Energy"] = YTArray(d["energy"][start_e:end_e], "keV")

        f.close()

        cosmo = Cosmology(hubble_constant=parameters["HubbleConstant"],
                          omega_matter=parameters["OmegaMatter"],
                          omega_lambda=parameters["OmegaLambda"])

        return cls(photons, parameters, cosmo, p_bins)

    @classmethod
    def from_scratch(cls, data_source, redshift, area,
                     exp_time, photon_model, parameters=None,
                     center=None, dist=None, cosmology=None):
        r"""
        Initialize a PhotonList from a photon model. The redshift, collecting area,
        exposure time, and cosmology are stored in the *parameters* dictionary which
        is passed to the *photon_model* function. 

        Parameters
        ----------
        data_source : `yt.data_objects.data_containers.YTSelectionContainer`
            The data source from which the photons will be generated.
        redshift : float
            The cosmological redshift for the photons.
        area : float
            The collecting area to determine the number of photons in cm^2.
        exp_time : float
            The exposure time to determine the number of photons in seconds.
        photon_model : function
            A function that takes the *data_source* and the *parameters*
            dictionary and returns a *photons* dictionary. Must be of the
            form: photon_model(data_source, parameters)
        parameters : dict, optional
            A dictionary of parameters to be passed to the user function.
        center : string or array_like, optional
            The origin of the photons. Accepts "c", "max", or a coordinate.
        dist : tuple, optional
            The angular diameter distance in the form (value, unit), used
            mainly for nearby sources. This may be optionally supplied
            instead of it being determined from the *redshift* and given *cosmology*.
        cosmology : `yt.utilities.cosmology.Cosmology`, optional
            Cosmological information. If not supplied, we try to get
            the cosmology from the dataset. Otherwise, \LambdaCDM with
            the default yt parameters is assumed.

        Examples
        --------
        This is the simplest possible example, where we call the built-in thermal model:

        >>> thermal_model = ThermalPhotonModel(apec_model, Zmet=0.3)
        >>> redshift = 0.05
        >>> area = 6000.0
        >>> time = 2.0e5
        >>> sp = ds.sphere("c", (500., "kpc"))
        >>> my_photons = PhotonList.from_user_model(sp, redshift, area,
        ...                                         time, thermal_model)

        If you wish to make your own photon model function, it must take as its
        arguments the *data_source* and the *parameters* dictionary. However you
        determine them, the *photons* dict needs to have the following items, corresponding
        to cells which have photons:

        "x" : the x-position of the cell relative to the source center in kpc, YTArray
        "y" : the y-position of the cell relative to the source center in kpc, YTArray
        "z" : the z-position of the cell relative to the source center in kpc, YTArray
        "vx" : the x-velocity of the cell in km/s, YTArray
        "vy" : the y-velocity of the cell in km/s, YTArray
        "vz" : the z-velocity of the cell in km/s, YTArray
        "dx" : the width of the cell in kpc, YTArray
        "NumberOfPhotons" : the number of photons in the cell, NumPy array of unsigned 64-bit integers
        "Energy" : the source rest-frame energies of the photons, YTArray

        The last array is not the same size as the others because it contains the energies in all of
        the cells in a single 1-D array. The first photons["NumberOfPhotons"][0] elements are
        for the first cell, the next photons["NumberOfPhotons"][1] are for the second cell, and so on.

        The following is a simple example where a point source with a single line emission
        spectrum of photons is created. More complicated examples which actually
        create photons based on the fields in the dataset could be created. 

        >>> import numpy as np
        >>> import yt
        >>> from yt.analysis_modules.photon_simulator.api import PhotonList
        >>> def line_func(source, parameters):
        ...
        ...     ds = source.ds
        ... 
        ...     num_photons = parameters["num_photons"]
        ...     E0  = parameters["line_energy"] # Energies are in keV
        ...     sigE = parameters["line_sigma"] 
        ...     src_ctr = parameters["center"]
        ...
        ...     energies = norm.rvs(loc=E0, scale=sigE, size=num_photons)
        ...
        ...     # Place everything in the center cell
        ...     for i, ax in enumerate("xyz"):
        ...         photons[ax] = (ds.domain_center[0]-src_ctr[0]).in_units("kpc")
        ...     photons["vx"] = ds.arr([0], "km/s")
        ...     photons["vy"] = ds.arr([0], "km/s")
        ...     photons["vz"] = ds.arr([100.0], "km/s")
        ...     photons["dx"] = ds.find_field_values_at_point("dx", ds.domain_center).in_units("kpc")
        ...     photons["NumberOfPhotons"] = np.array(num_photons*np.ones(1), dtype="uint64")
        ...     photons["Energy"] = ds.arr(energies, "keV")
        >>>
        >>> redshift = 0.05
        >>> area = 6000.0
        >>> time = 2.0e5
        >>> parameters = {"num_photons" : 10000, "line_energy" : 5.0,
        ...               "line_sigma" : 0.1}
        >>> ddims = (128,128,128)
        >>> random_data = {"density":(np.random.random(ddims),"g/cm**3")}
        >>> ds = yt.load_uniform_grid(random_data, ddims)
        >>> dd = ds.all_data()
        >>> my_photons = PhotonList.from_user_model(dd, redshift, area,
        ...                                         time, line_func,
        ...                                         parameters=parameters)

        """

        ds = data_source.ds

        if parameters is None:
             parameters = {}
        if cosmology is None:
            hubble = getattr(ds, "hubble_constant", None)
            omega_m = getattr(ds, "omega_matter", None)
            omega_l = getattr(ds, "omega_lambda", None)
            if hubble == 0: hubble = None
            if hubble is not None and \
               omega_m is not None and \
               omega_l is not None:
                cosmo = Cosmology(hubble_constant=hubble,
                                  omega_matter=omega_m,
                                  omega_lambda=omega_l)
            else:
                cosmo = Cosmology()
        else:
            cosmo = cosmology
        mylog.info("Cosmology: h = %g, omega_matter = %g, omega_lambda = %g" %
                   (cosmo.hubble_constant, cosmo.omega_matter, cosmo.omega_lambda))
        if dist is None:
            D_A = cosmo.angular_diameter_distance(0.0,redshift).in_units("Mpc")
        else:
            D_A = parse_value(dist, "Mpc")
            redshift = 0.0

        if center in ("center", "c"):
            parameters["center"] = ds.domain_center
        elif center in ("max", "m"):
            parameters["center"] = ds.find_max("density")[-1]
        elif iterable(center):
            if isinstance(center, YTArray):
                parameters["center"] = center.in_units("code_length")
            elif isinstance(center, tuple):
                if center[0] == "min":
                    parameters["center"] = ds.find_min(center[1])[-1]
                elif center[0] == "max":
                    parameters["center"] = ds.find_max(center[1])[-1]
                else:
                    raise RuntimeError
            else:
                parameters["center"] = ds.arr(center, "code_length")
        elif center is None:
            parameters["center"] = data_source.get_field_parameter("center")

        parameters["FiducialExposureTime"] = parse_value(exp_time, "s")
        parameters["FiducialArea"] = parse_value(area, "cm**2")
        parameters["FiducialRedshift"] = redshift
        parameters["FiducialAngularDiameterDistance"] = D_A
        parameters["HubbleConstant"] = cosmo.hubble_constant
        parameters["OmegaMatter"] = cosmo.omega_matter
        parameters["OmegaLambda"] = cosmo.omega_lambda

        dimension = 0
        width = 0.0
        for i, ax in enumerate("xyz"):
            le, re = data_source.quantities.extrema(ax)
            delta_min, delta_max = data_source.quantities.extrema("d%s"%ax)
            le -= 0.5*delta_max
            re += 0.5*delta_max
            width = max(width, re-parameters["center"][i], parameters["center"][i]-le)
            dimension = max(dimension, int(width/delta_min))
        parameters["Dimension"] = 2*dimension
        parameters["Width"] = 2.*width.in_units("kpc")

        photons = photon_model(data_source, parameters)

        mylog.info("Finished generating photons.")

        p_bins = np.cumsum(photons["NumberOfPhotons"])
        p_bins = np.insert(p_bins, 0, [np.uint64(0)])

        return cls(photons, parameters, cosmo, p_bins)

    def write_h5_file(self, photonfile):
        """
        Write the photons to the HDF5 file *photonfile*.
        """

        if parallel_capable:

            mpi_long = get_mpi_type("int64")
            mpi_double = get_mpi_type("float64")

            local_num_cells = len(self.photons["x"])
            sizes_c = comm.comm.gather(local_num_cells, root=0)

            local_num_photons = np.sum(self.photons["NumberOfPhotons"])
            sizes_p = comm.comm.gather(local_num_photons, root=0)

            if comm.rank == 0:
                num_cells = sum(sizes_c)
                num_photons = sum(sizes_p)
                disps_c = [sum(sizes_c[:i]) for i in range(len(sizes_c))]
                disps_p = [sum(sizes_p[:i]) for i in range(len(sizes_p))]
                x = np.zeros(num_cells)
                y = np.zeros(num_cells)
                z = np.zeros(num_cells)
                vx = np.zeros(num_cells)
                vy = np.zeros(num_cells)
                vz = np.zeros(num_cells)
                dx = np.zeros(num_cells)
                n_ph = np.zeros(num_cells, dtype="uint64")
                e = np.zeros(num_photons)
            else:
                sizes_c = []
                sizes_p = []
                disps_c = []
                disps_p = []
                x = np.empty([])
                y = np.empty([])
                z = np.empty([])
                vx = np.empty([])
                vy = np.empty([])
                vz = np.empty([])
                dx = np.empty([])
                n_ph = np.empty([])
                e = np.empty([])

            comm.comm.Gatherv([self.photons["x"].d, local_num_cells, mpi_double],
                              [x, (sizes_c, disps_c), mpi_double], root=0)
            comm.comm.Gatherv([self.photons["y"].d, local_num_cells, mpi_double],
                              [y, (sizes_c, disps_c), mpi_double], root=0)
            comm.comm.Gatherv([self.photons["z"].d, local_num_cells, mpi_double],
                              [z, (sizes_c, disps_c), mpi_double], root=0)
            comm.comm.Gatherv([self.photons["vx"].d, local_num_cells, mpi_double],
                              [vx, (sizes_c, disps_c), mpi_double], root=0)
            comm.comm.Gatherv([self.photons["vy"].d, local_num_cells, mpi_double],
                              [vy, (sizes_c, disps_c), mpi_double], root=0)
            comm.comm.Gatherv([self.photons["vz"].d, local_num_cells, mpi_double],
                              [vz, (sizes_c, disps_c), mpi_double], root=0)
            comm.comm.Gatherv([self.photons["dx"].d, local_num_cells, mpi_double],
                              [dx, (sizes_c, disps_c), mpi_double], root=0)
            comm.comm.Gatherv([self.photons["NumberOfPhotons"], local_num_cells, mpi_long],
                              [n_ph, (sizes_c, disps_c), mpi_long], root=0)
            comm.comm.Gatherv([self.photons["Energy"].d, local_num_photons, mpi_double],
                              [e, (sizes_p, disps_p), mpi_double], root=0)

        else:

            x = self.photons["x"].d
            y = self.photons["y"].d
            z = self.photons["z"].d
            vx = self.photons["vx"].d
            vy = self.photons["vy"].d
            vz = self.photons["vz"].d
            dx = self.photons["dx"].d
            n_ph = self.photons["NumberOfPhotons"]
            e = self.photons["Energy"].d

        if comm.rank == 0:

            f = h5py.File(photonfile, "w")

            # Parameters

            p = f.create_group("parameters")
            p.create_dataset("fid_area", data=float(self.parameters["FiducialArea"]))
            p.create_dataset("fid_exp_time", data=float(self.parameters["FiducialExposureTime"]))
            p.create_dataset("fid_redshift", data=self.parameters["FiducialRedshift"])
            p.create_dataset("hubble", data=self.parameters["HubbleConstant"])
            p.create_dataset("omega_matter", data=self.parameters["OmegaMatter"])
            p.create_dataset("omega_lambda", data=self.parameters["OmegaLambda"])
            p.create_dataset("fid_d_a", data=float(self.parameters["FiducialAngularDiameterDistance"]))
            p.create_dataset("dimension", data=self.parameters["Dimension"])
            p.create_dataset("width", data=float(self.parameters["Width"]))

            # Data

            d = f.create_group("data")
            d.create_dataset("x", data=x)
            d.create_dataset("y", data=y)
            d.create_dataset("z", data=z)
            d.create_dataset("vx", data=vx)
            d.create_dataset("vy", data=vy)
            d.create_dataset("vz", data=vz)
            d.create_dataset("dx", data=dx)
            d.create_dataset("num_photons", data=n_ph)
            d.create_dataset("energy", data=e)

            f.close()

        comm.barrier()

    def project_photons(self, normal, area_new=None, exp_time_new=None,
                        redshift_new=None, dist_new=None,
                        absorb_model=None, psf_sigma=None,
                        sky_center=None, responses=None,
                        convolve_energies=False, no_shifting=False,
                        north_vector=None, prng=np.random):
        r"""
        Projects photons onto an image plane given a line of sight.

        Parameters
        ----------
        normal : character or array_like
            Normal vector to the plane of projection. If "x", "y", or "z", will
            assume to be along that axis (and will probably be faster). Otherwise, 
            should be an off-axis normal vector, e.g [1.0,2.0,-3.0]
        area_new : float, optional
            New value for the effective area of the detector. If *responses*
            are specified the value of this keyword is ignored.
        exp_time_new : float, optional
            The new value for the exposure time.
        redshift_new : float, optional
            The new value for the cosmological redshift.
        dist_new : tuple, optional
            The new value for the angular diameter distance in the form
            (value, unit), used mainly for nearby sources. This may be optionally supplied
            instead of it being determined from the cosmology.
        absorb_model : 'yt.analysis_modules.photon_simulator.PhotonModel`, optional
            A model for galactic absorption.
        psf_sigma : float, optional
            Quick-and-dirty psf simulation using Gaussian smoothing with
            standard deviation *psf_sigma* in degrees. 
        sky_center : array_like, optional
            Center RA, Dec of the events in degrees.
        responses : list of strings, optional
            The names of the ARF and/or RMF files to convolve the photons with.
        convolve_energies : boolean, optional
            If this is set, the photon energies will be convolved with the RMF.
        no_shifting : boolean, optional
            If set, the photon energies will not be Doppler shifted.
        north_vector : a sequence of floats
            A vector defining the 'up' direction. This option sets the orientation of 
            the plane of projection. If not set, an arbitrary grid-aligned north_vector 
            is chosen. Ignored in the case where a particular axis (e.g., "x", "y", or
            "z") is explicitly specified.
        prng : NumPy `RandomState` object or numpy.random                                                                              
            A pseudo-random number generator. Typically will only be specified if you
            have a reason to generate the same set of random numbers, such as for a                                    
            test. Default is the numpy.random module.                                                                            

        Examples
        --------
        >>> L = np.array([0.1,-0.2,0.3])
        >>> events = my_photons.project_photons(L, area_new="sim_arf.fits",
        ...                                     redshift_new=0.05,
        ...                                     psf_sigma=0.01)
        """

        if redshift_new is not None and dist_new is not None:
            mylog.error("You may specify a new redshift or distance, "+
                        "but not both!")

        if sky_center is None:
            sky_center = YTArray([30.,45.], "degree")
        else:
            sky_center = YTArray(sky_center, "degree")

        dx = self.photons["dx"].d
        nx = self.parameters["Dimension"]
        if psf_sigma is not None:
             psf_sigma = parse_value(psf_sigma, "degree")

        if not isinstance(normal, string_types):
            L = np.array(normal)
            orient = Orientation(L, north_vector=north_vector)
            x_hat = orient.unit_vectors[0]
            y_hat = orient.unit_vectors[1]
            z_hat = orient.unit_vectors[2]

        n_ph = self.photons["NumberOfPhotons"]
        n_ph_tot = n_ph.sum()

        eff_area = None

        parameters = {}

        if responses is not None:
            responses = ensure_list(responses)
            parameters["ARF"] = responses[0]
            if len(responses) == 2:
                parameters["RMF"] = responses[1]
            area_new = parameters["ARF"]

        zobs0 = self.parameters["FiducialRedshift"]
        D_A0 = self.parameters["FiducialAngularDiameterDistance"]
        scale_factor = 1.0

        # If we use an RMF, figure out where the response matrix actually is.
        if "RMF" in parameters:
            rmf = _astropy.pyfits.open(parameters["RMF"])
            if "MATRIX" in rmf:
                mat_key = "MATRIX"
            elif "SPECRESP MATRIX" in rmf:
                mat_key = "SPECRESP MATRIX"
            else:
                raise RuntimeError("Cannot find the response matrix in the RMF "
                                   "file %s! " % parameters["RMF"]+"It should "
                                   "be named \"MATRIX\" or \"SPECRESP MATRIX\".")
            rmf.close()
        else:
            mat_key = None

        if (exp_time_new is None and area_new is None and
            redshift_new is None and dist_new is None):
            my_n_obs = n_ph_tot
            zobs = zobs0
            D_A = D_A0
        else:
            if exp_time_new is None:
                Tratio = 1.
            else:
                Tratio = parse_value(exp_time_new, "s")/self.parameters["FiducialExposureTime"]
            if area_new is None:
                Aratio = 1.
            elif isinstance(area_new, string_types):
                if comm.rank == 0:
                    mylog.info("Using energy-dependent effective area: %s" % (parameters["ARF"]))
                f = _astropy.pyfits.open(area_new)
                earf = 0.5*(f["SPECRESP"].data.field("ENERG_LO")+f["SPECRESP"].data.field("ENERG_HI"))
                eff_area = np.nan_to_num(f["SPECRESP"].data.field("SPECRESP"))
                if "RMF" in parameters:
                    weights = self._normalize_arf(parameters["RMF"], mat_key)
                    eff_area *= weights
                else:
                    mylog.warning("You specified an ARF but not an RMF. This is ok if the "+
                                  "responses are normalized properly. If not, you may "+
                                  "get inconsistent results.")
                f.close()
                Aratio = eff_area.max()/self.parameters["FiducialArea"].v
            else:
                mylog.info("Using constant effective area.")
                Aratio = parse_value(area_new, "cm**2")/self.parameters["FiducialArea"]
            if redshift_new is None and dist_new is None:
                Dratio = 1.
                zobs = zobs0
                D_A = D_A0
            else:
                if redshift_new is None:
                    zobs = 0.0
                    D_A = parse_value(dist_new, "Mpc")
                else:
                    zobs = redshift_new
                    D_A = self.cosmo.angular_diameter_distance(0.0,zobs).in_units("Mpc")
                    scale_factor = (1.+zobs0)/(1.+zobs)
                Dratio = D_A0*D_A0*(1.+zobs0)**3 / \
                         (D_A*D_A*(1.+zobs)**3)
            fak = Aratio*Tratio*Dratio
            if fak > 1:
                raise ValueError("This combination of requested parameters results in "
                                 "%g%% more photons collected than are " % (100.*(fak-1.)) +
                                 "available in the sample. Please reduce the collecting "
                                 "area, exposure time, or increase the distance/redshift "
                                 "of the object. Alternatively, generate a larger sample "
                                 "of photons.")
            my_n_obs = np.uint64(n_ph_tot*fak)

        n_obs_all = comm.mpi_allreduce(my_n_obs)
        if comm.rank == 0:
            mylog.info("Total number of photons to use: %d" % (n_obs_all))

        if my_n_obs == n_ph_tot:
            idxs = np.arange(my_n_obs,dtype='uint64')
        else:
            idxs = prng.permutation(n_ph_tot)[:my_n_obs].astype("uint64")
        obs_cells = np.searchsorted(self.p_bins, idxs, side='right')-1
        delta = dx[obs_cells]

        if isinstance(normal, string_types):

            xsky = prng.uniform(low=-0.5,high=0.5,size=my_n_obs)
            ysky = prng.uniform(low=-0.5,high=0.5,size=my_n_obs)
            xsky *= delta
            ysky *= delta
            xsky += self.photons[axes_lookup[normal][0]][obs_cells]
            ysky += self.photons[axes_lookup[normal][1]][obs_cells]

            if not no_shifting:
                vz = self.photons["v%s" % normal]

        else:
            x = prng.uniform(low=-0.5,high=0.5,size=my_n_obs)
            y = prng.uniform(low=-0.5,high=0.5,size=my_n_obs)
            z = prng.uniform(low=-0.5,high=0.5,size=my_n_obs)

            if not no_shifting:
                vz = self.photons["vx"]*z_hat[0] + \
                     self.photons["vy"]*z_hat[1] + \
                     self.photons["vz"]*z_hat[2]

            x *= delta
            y *= delta
            z *= delta
            x += self.photons["x"][obs_cells].d
            y += self.photons["y"][obs_cells].d
            z += self.photons["z"][obs_cells].d

            xsky = x*x_hat[0] + y*x_hat[1] + z*x_hat[2]
            ysky = x*y_hat[0] + y*y_hat[1] + z*y_hat[2]

        if no_shifting:
            eobs = self.photons["Energy"][idxs]
        else:
            shift = -vz.in_cgs()/clight
            shift = np.sqrt((1.-shift)/(1.+shift))
            eobs = self.photons["Energy"][idxs]*shift[obs_cells]
        eobs *= scale_factor

        if absorb_model is None:
            not_abs = np.ones(eobs.shape, dtype='bool')
        else:
            mylog.info("Absorbing.")
            absorb_model.prepare_spectrum()
            emid = absorb_model.emid
            aspec = absorb_model.get_spectrum()
            absorb = np.interp(eobs, emid, aspec, left=0.0, right=0.0)
            randvec = aspec.max()*prng.uniform(size=eobs.shape)
            not_abs = randvec < absorb
            absorb_model.cleanup_spectrum()

        if eff_area is None:
            detected = np.ones(eobs.shape, dtype='bool')
        else:
            mylog.info("Applying energy-dependent effective area.")
            earea = np.interp(eobs, earf, eff_area, left=0.0, right=0.0)
            randvec = eff_area.max()*prng.uniform(size=eobs.shape)
            detected = randvec < earea

        detected = np.logical_and(not_abs, detected)

        events = {}

        dx_min = self.parameters["Width"]/self.parameters["Dimension"]
        dtheta = YTQuantity(np.rad2deg(dx_min/D_A), "degree")

        events["xpix"] = xsky[detected]/dx_min.v + 0.5*(nx+1)
        events["ypix"] = ysky[detected]/dx_min.v + 0.5*(nx+1)
        events["eobs"] = eobs[detected]

        events = comm.par_combine_object(events, datatype="dict", op="cat")

        if psf_sigma is not None:
            events["xpix"] += prng.normal(sigma=psf_sigma/dtheta)
            events["ypix"] += prng.normal(sigma=psf_sigma/dtheta)

        num_events = len(events["xpix"])

        if comm.rank == 0:
            mylog.info("Total number of observed photons: %d" % num_events)

        if "RMF" in parameters and convolve_energies:
            events, info = self._convolve_with_rmf(parameters["RMF"], events, 
                                                   mat_key, prng)
            for k, v in info.items():
                parameters[k] = v

        if exp_time_new is None:
            parameters["ExposureTime"] = self.parameters["FiducialExposureTime"]
        else:
            parameters["ExposureTime"] = exp_time_new
        if area_new is None:
            parameters["Area"] = self.parameters["FiducialArea"]
        else:
            parameters["Area"] = area_new
        parameters["Redshift"] = zobs
        parameters["AngularDiameterDistance"] = D_A.in_units("Mpc")
        parameters["sky_center"] = sky_center
        parameters["pix_center"] = np.array([0.5*(nx+1)]*2)
        parameters["dtheta"] = dtheta

        return EventList(events, parameters)

    def _normalize_arf(self, respfile, mat_key):
        rmf = _astropy.pyfits.open(respfile)
        table = rmf[mat_key]
        weights = np.array([w.sum() for w in table.data["MATRIX"]])
        rmf.close()
        return weights

    def _convolve_with_rmf(self, respfile, events, mat_key, prng):
        """
        Convolve the events with a RMF file.
        """
        mylog.info("Reading response matrix file (RMF): %s" % (respfile))

        hdulist = _astropy.pyfits.open(respfile)

        tblhdu = hdulist[mat_key]
        n_de = len(tblhdu.data["ENERG_LO"])
        mylog.info("Number of energy bins in RMF: %d" % (n_de))
        mylog.info("Energy limits: %g %g" % (min(tblhdu.data["ENERG_LO"]),
                                             max(tblhdu.data["ENERG_HI"])))

        tblhdu2 = hdulist["EBOUNDS"]
        n_ch = len(tblhdu2.data["CHANNEL"])
        mylog.info("Number of channels in RMF: %d" % (n_ch))

        eidxs = np.argsort(events["eobs"])

        phEE = events["eobs"][eidxs].d

        detectedChannels = []

        # run through all photon energies and find which bin they go in
        k = 0
        fcurr = 0
        last = len(phEE)

        pbar = get_pbar("Scattering energies with RMF:", last)

        for low,high in zip(tblhdu.data["ENERG_LO"],tblhdu.data["ENERG_HI"]):
            # weight function for probabilities from RMF
            weights = np.nan_to_num(np.float64(tblhdu.data[k]["MATRIX"][:]))
            weights /= weights.sum()
            # build channel number list associated to array value,
            # there are groups of channels in rmfs with nonzero probabilities
            trueChannel = []
            f_chan = np.nan_to_num(tblhdu.data["F_CHAN"][k])
            n_chan = np.nan_to_num(tblhdu.data["N_CHAN"][k])
            n_grp = np.nan_to_num(tblhdu.data["N_CHAN"][k])
            if not iterable(f_chan):
                f_chan = [f_chan]
                n_chan = [n_chan]
                n_grp  = [n_grp]
            for start,nchan in zip(f_chan, n_chan):
                end = start + nchan
                if start == end:
                    trueChannel.append(start)
                else:
                    for j in range(start,end):
                        trueChannel.append(j)
            if len(trueChannel) > 0:
                for q in range(fcurr,last):
                    if phEE[q] >= low and phEE[q] < high:
                        channelInd = prng.choice(len(weights), p=weights)
                        fcurr += 1
                        detectedChannels.append(trueChannel[channelInd])
                    if phEE[q] >= high:
                        break
            pbar.update(fcurr)
            k += 1
        pbar.finish()

        dchannel = np.array(detectedChannels)

        events["xpix"] = events["xpix"][eidxs]
        events["ypix"] = events["ypix"][eidxs]
        events["eobs"] = YTArray(phEE, "keV")
        events[tblhdu.header["CHANTYPE"]] = dchannel.astype(int)

        info = {"ChannelType" : tblhdu.header["CHANTYPE"],
                "Telescope" : tblhdu.header["TELESCOP"],
                "Instrument" : tblhdu.header["INSTRUME"]}

        info["Mission"] = tblhdu.header.get("MISSION","")

        return events, info

class EventList(object):

    def __init__(self, events, parameters):
        self.events = events
        self.parameters = parameters
        self.num_events = events["xpix"].shape[0]
        self.wcs = _astropy.pywcs.WCS(naxis=2)
        self.wcs.wcs.crpix = parameters["pix_center"]
        self.wcs.wcs.crval = parameters["sky_center"].d
        self.wcs.wcs.cdelt = [-parameters["dtheta"].value, parameters["dtheta"].value]
        self.wcs.wcs.ctype = ["RA---TAN","DEC--TAN"]
        self.wcs.wcs.cunit = ["deg"]*2

    def keys(self):
        return self.events.keys()

    def has_key(self, key):
        return key in self.keys()

    def items(self):
        return self.events.items()

    def values(self):
        return self.events.values()

    def __getitem__(self,key):
        if key not in self.events:
            if key == "xsky" or key == "ysky":
                x,y = self.wcs.wcs_pix2world(self.events["xpix"], self.events["ypix"], 1)
                self.events["xsky"] = YTArray(x, "degree")
                self.events["ysky"] = YTArray(y, "degree")
        return self.events[key]

    def __repr__(self):
        return self.events.__repr__()

    def __contains__(self, key):
        return key in self.events

    def __add__(self, other):
        assert_same_wcs(self.wcs, other.wcs)
        validate_parameters(self.parameters, other.parameters)
        events = {}
        for item1, item2 in zip(self.items(), other.items()):
            k1, v1 = item1
            k2, v2 = item2
            events[k1] = uconcatenate([v1,v2])
        return EventList(events, self.parameters)

    def filter_events(self, region):
        """
        Filter events using a ds9 *region*. Requires the pyregion package.
        Returns a new EventList.
        """
        import pyregion
        import os
        if os.path.exists(region):
            reg = pyregion.open(region)
        else:
            reg = pyregion.parse(region)
        r = reg.as_imagecoord(header=self.wcs.to_header())
        f = r.get_filter()
        idxs = f.inside_x_y(self["xpix"], self["ypix"])
        if idxs.sum() == 0:
            raise RuntimeError("No events are inside this region!")
        new_events = {}
        for k, v in self.items():
            new_events[k] = v[idxs]
        return EventList(new_events, self.parameters)

    @classmethod
    def from_h5_file(cls, h5file):
        """
        Initialize an EventList from a HDF5 file with filename *h5file*.
        """
        events = {}
        parameters = {}

        f = h5py.File(h5file, "r")

        p = f["/parameters"]
        parameters["ExposureTime"] = YTQuantity(p["exp_time"].value, "s")
        area = force_unicode(p['area'].value)
        if isinstance(area, string_types):
            parameters["Area"] = area
        else:
            parameters["Area"] = YTQuantity(area, "cm**2")
        parameters["Redshift"] = p["redshift"].value
        parameters["AngularDiameterDistance"] = YTQuantity(p["d_a"].value, "Mpc")
        parameters["sky_center"] = YTArray(p["sky_center"][:], "deg")
        parameters["dtheta"] = YTQuantity(p["dtheta"].value, "deg")
        parameters["pix_center"] = p["pix_center"][:]
        if "rmf" in p:
            parameters["RMF"] = force_unicode(p["rmf"].value)
        if "arf" in p:
            parameters["ARF"] = force_unicode(p["arf"].value)
        if "channel_type" in p:
            parameters["ChannelType"] = force_unicode(p["channel_type"].value)
        if "mission" in p:
            parameters["Mission"] = force_unicode(p["mission"].value)
        if "telescope" in p:
            parameters["Telescope"] = force_unicode(p["telescope"].value)
        if "instrument" in p:
            parameters["Instrument"] = force_unicode(p["instrument"].value)

        d = f["/data"]
        events["xpix"] = d["xpix"][:]
        events["ypix"] = d["ypix"][:]
        events["eobs"] = YTArray(d["eobs"][:], "keV")
        if "pi" in d:
            events["PI"] = d["pi"][:]
        if "pha" in d:
            events["PHA"] = d["pha"][:]

        f.close()

        return cls(events, parameters)

    @classmethod
    def from_fits_file(cls, fitsfile):
        """
        Initialize an EventList from a FITS file with filename *fitsfile*.
        """
        hdulist = _astropy.pyfits.open(fitsfile)

        tblhdu = hdulist["EVENTS"]

        events = {}
        parameters = {}

        parameters["ExposureTime"] = YTQuantity(tblhdu.header["EXPOSURE"], "s")
        if isinstance(tblhdu.header["AREA"], (string_types, bytes)):
            parameters["Area"] = tblhdu.header["AREA"]
        else:
            parameters["Area"] = YTQuantity(tblhdu.header["AREA"], "cm**2")
        parameters["Redshift"] = tblhdu.header["REDSHIFT"]
        parameters["AngularDiameterDistance"] = YTQuantity(tblhdu.header["D_A"], "Mpc")
        if "RMF" in tblhdu.header:
            parameters["RMF"] = tblhdu["RMF"]
        if "ARF" in tblhdu.header:
            parameters["ARF"] = tblhdu["ARF"]
        if "CHANTYPE" in tblhdu.header:
            parameters["ChannelType"] = tblhdu["CHANTYPE"]
        if "MISSION" in tblhdu.header:
            parameters["Mission"] = tblhdu["MISSION"]
        if "TELESCOP" in tblhdu.header:
            parameters["Telescope"] = tblhdu["TELESCOP"]
        if "INSTRUME" in tblhdu.header:
            parameters["Instrument"] = tblhdu["INSTRUME"]
        parameters["sky_center"] = YTArray([tblhdu["TCRVL2"],tblhdu["TCRVL3"]], "deg")
        parameters["pix_center"] = np.array([tblhdu["TCRVL2"],tblhdu["TCRVL3"]])
        parameters["dtheta"] = YTQuantity(tblhdu["TCRVL3"], "deg")
        events["xpix"] = tblhdu.data.field("X")
        events["ypix"] = tblhdu.data.field("Y")
        events["eobs"] = YTArray(tblhdu.data.field("ENERGY")/1000., "keV")
        if "PI" in tblhdu.columns.names:
            events["PI"] = tblhdu.data.field("PI")
        if "PHA" in tblhdu.columns.names:
            events["PHA"] = tblhdu.data.field("PHA")

        return cls(events, parameters)

    @parallel_root_only
    def write_fits_file(self, fitsfile, clobber=False):
        """
        Write events to a FITS binary table file with filename *fitsfile*.
        Set *clobber* to True if you need to overwrite a previous file.
        """
        pyfits = _astropy.pyfits
        Time = _astropy.time.Time
        TimeDelta = _astropy.time.TimeDelta

        exp_time = float(self.parameters["ExposureTime"])

        t_begin = Time.now()
        dt = TimeDelta(exp_time, format='sec')
        t_end = t_begin + dt

        cols = []

        col_e = pyfits.Column(name='ENERGY', format='E', unit='eV',
                              array=self["eobs"].in_units("eV").d)
        col_x = pyfits.Column(name='X', format='D', unit='pixel',
                              array=self["xpix"])
        col_y = pyfits.Column(name='Y', format='D', unit='pixel',
                              array=self["ypix"])

        cols = [col_e, col_x, col_y]

        if "ChannelType" in self.parameters:
             chantype = self.parameters["ChannelType"]
             if chantype == "PHA":
                  cunit = "adu"
             elif chantype == "PI":
                  cunit = "Chan"
             col_ch = pyfits.Column(name=chantype.upper(), format='1J',
                                    unit=cunit, array=self.events[chantype])
             cols.append(col_ch)

             mylog.info("Generating times for events assuming uniform time "
                        "distribution. In future versions this will be made "
                        "more general.")

             time = np.random.uniform(size=self.num_events, low=0.0,
                                      high=float(self.parameters["ExposureTime"]))
             col_t = pyfits.Column(name="TIME", format='1D', unit='s',
                                   array=time)
             cols.append(col_t)

        coldefs = pyfits.ColDefs(cols)
        tbhdu = pyfits.BinTableHDU.from_columns(coldefs)
        tbhdu.update_ext_name("EVENTS")

        tbhdu.header["MTYPE1"] = "sky"
        tbhdu.header["MFORM1"] = "x,y"
        tbhdu.header["MTYPE2"] = "EQPOS"
        tbhdu.header["MFORM2"] = "RA,DEC"
        tbhdu.header["TCTYP2"] = "RA---TAN"
        tbhdu.header["TCTYP3"] = "DEC--TAN"
        tbhdu.header["TCRVL2"] = float(self.parameters["sky_center"][0])
        tbhdu.header["TCRVL3"] = float(self.parameters["sky_center"][1])
        tbhdu.header["TCDLT2"] = -float(self.parameters["dtheta"])
        tbhdu.header["TCDLT3"] = float(self.parameters["dtheta"])
        tbhdu.header["TCRPX2"] = self.parameters["pix_center"][0]
        tbhdu.header["TCRPX3"] = self.parameters["pix_center"][1]
        tbhdu.header["TLMIN2"] = 0.5
        tbhdu.header["TLMIN3"] = 0.5
        tbhdu.header["TLMAX2"] = 2.*self.parameters["pix_center"][0]-0.5
        tbhdu.header["TLMAX3"] = 2.*self.parameters["pix_center"][1]-0.5
        tbhdu.header["EXPOSURE"] = exp_time
        tbhdu.header["TSTART"] = 0.0
        tbhdu.header["TSTOP"] = exp_time
        if isinstance(self.parameters["Area"], string_types):
            tbhdu.header["AREA"] = self.parameters["Area"]
        else:
            tbhdu.header["AREA"] = float(self.parameters["Area"])
        tbhdu.header["D_A"] = float(self.parameters["AngularDiameterDistance"])
        tbhdu.header["REDSHIFT"] = self.parameters["Redshift"]
        tbhdu.header["HDUVERS"] = "1.1.0"
        tbhdu.header["RADECSYS"] = "FK5"
        tbhdu.header["EQUINOX"] = 2000.0
        tbhdu.header["HDUCLASS"] = "OGIP"
        tbhdu.header["HDUCLAS1"] = "EVENTS"
        tbhdu.header["HDUCLAS2"] = "ACCEPTED"
        tbhdu.header["DATE"] = t_begin.tt.isot
        tbhdu.header["DATE-OBS"] = t_begin.tt.isot
        tbhdu.header["DATE-END"] = t_end.tt.isot
        if "RMF" in self.parameters:
            tbhdu.header["RESPFILE"] = self.parameters["RMF"]
            f = pyfits.open(self.parameters["RMF"])
            nchan = int(f["EBOUNDS"].header["DETCHANS"])
            tbhdu.header["PHA_BINS"] = nchan
            f.close()
        if "ARF" in self.parameters:
            tbhdu.header["ANCRFILE"] = self.parameters["ARF"]
        if "ChannelType" in self.parameters:
            tbhdu.header["CHANTYPE"] = self.parameters["ChannelType"]
        if "Mission" in self.parameters:
            tbhdu.header["MISSION"] = self.parameters["Mission"]
        if "Telescope" in self.parameters:
            tbhdu.header["TELESCOP"] = self.parameters["Telescope"]
        if "Instrument" in self.parameters:
            tbhdu.header["INSTRUME"] = self.parameters["Instrument"]

        hdulist = [pyfits.PrimaryHDU(), tbhdu]

        if "ChannelType" in self.parameters:
            start = pyfits.Column(name='START', format='1D', unit='s',
                                  array=np.array([0.0]))
            stop = pyfits.Column(name='STOP', format='1D', unit='s',
                                 array=np.array([exp_time]))

            tbhdu_gti = pyfits.BinTableHDU.from_columns([start,stop])
            tbhdu_gti.update_ext_name("STDGTI")
            tbhdu_gti.header["TSTART"] = 0.0
            tbhdu_gti.header["TSTOP"] = exp_time
            tbhdu_gti.header["HDUCLASS"] = "OGIP"
            tbhdu_gti.header["HDUCLAS1"] = "GTI"
            tbhdu_gti.header["HDUCLAS2"] = "STANDARD"
            tbhdu_gti.header["RADECSYS"] = "FK5"
            tbhdu_gti.header["EQUINOX"] = 2000.0
            tbhdu_gti.header["DATE"] = t_begin.tt.isot
            tbhdu_gti.header["DATE-OBS"] = t_begin.tt.isot
            tbhdu_gti.header["DATE-END"] = t_end.tt.isot

            hdulist.append(tbhdu_gti)

        pyfits.HDUList(hdulist).writeto(fitsfile, clobber=clobber)

    @parallel_root_only
    def write_simput_file(self, prefix, clobber=False, emin=None, emax=None):
        r"""
        Write events to a SIMPUT file that may be read by the SIMX instrument
        simulator.

        Parameters
        ----------
        prefix : string
            The filename prefix.
        clobber : boolean, optional
            Set to True to overwrite previous files.
        e_min : float, optional
            The minimum energy of the photons to save in keV.
        e_max : float, optional
            The maximum energy of the photons to save in keV.
        """
        pyfits = _astropy.pyfits
        if isinstance(self.parameters["Area"], string_types):
             mylog.error("Writing SIMPUT files is only supported if you didn't convolve with responses.")
             raise TypeError("Writing SIMPUT files is only supported if you didn't convolve with responses.")

        if emin is None:
            emin = self["eobs"].min().value
        if emax is None:
            emax = self["eobs"].max().value

        idxs = np.logical_and(self["eobs"].d >= emin, self["eobs"].d <= emax)
        flux = np.sum(self["eobs"][idxs].in_units("erg")) / \
               self.parameters["ExposureTime"]/self.parameters["Area"]

        col1 = pyfits.Column(name='ENERGY', format='E', array=self["eobs"].d)
        col2 = pyfits.Column(name='DEC', format='D', array=self["ysky"].d)
        col3 = pyfits.Column(name='RA', format='D', array=self["xsky"].d)

        coldefs = pyfits.ColDefs([col1, col2, col3])

        tbhdu = pyfits.BinTableHDU.from_columns(coldefs)
        tbhdu.update_ext_name("PHLIST")

        tbhdu.header["HDUCLASS"] = "HEASARC/SIMPUT"
        tbhdu.header["HDUCLAS1"] = "PHOTONS"
        tbhdu.header["HDUVERS"] = "1.1.0"
        tbhdu.header["EXTVER"] = 1
        tbhdu.header["REFRA"] = 0.0
        tbhdu.header["REFDEC"] = 0.0
        tbhdu.header["TUNIT1"] = "keV"
        tbhdu.header["TUNIT2"] = "deg"
        tbhdu.header["TUNIT3"] = "deg"

        phfile = prefix+"_phlist.fits"

        tbhdu.writeto(phfile, clobber=clobber)

        col1 = pyfits.Column(name='SRC_ID', format='J', array=np.array([1]).astype("int32"))
        col2 = pyfits.Column(name='RA', format='D', array=np.array([0.0]))
        col3 = pyfits.Column(name='DEC', format='D', array=np.array([0.0]))
        col4 = pyfits.Column(name='E_MIN', format='D', array=np.array([float(emin)]))
        col5 = pyfits.Column(name='E_MAX', format='D', array=np.array([float(emax)]))
        col6 = pyfits.Column(name='FLUX', format='D', array=np.array([flux.value]))
        col7 = pyfits.Column(name='SPECTRUM', format='80A', array=np.array([phfile+"[PHLIST,1]"]))
        col8 = pyfits.Column(name='IMAGE', format='80A', array=np.array([phfile+"[PHLIST,1]"]))
        col9 = pyfits.Column(name='SRC_NAME', format='80A', array=np.array(["yt_src"]))

        coldefs = pyfits.ColDefs([col1, col2, col3, col4, col5, col6, col7, col8, col9])

        wrhdu = pyfits.BinTableHDU.from_columns(coldefs)
        wrhdu.update_ext_name("SRC_CAT")

        wrhdu.header["HDUCLASS"] = "HEASARC"
        wrhdu.header["HDUCLAS1"] = "SIMPUT"
        wrhdu.header["HDUCLAS2"] = "SRC_CAT"
        wrhdu.header["HDUVERS"] = "1.1.0"
        wrhdu.header["RADECSYS"] = "FK5"
        wrhdu.header["EQUINOX"] = 2000.0
        wrhdu.header["TUNIT2"] = "deg"
        wrhdu.header["TUNIT3"] = "deg"
        wrhdu.header["TUNIT4"] = "keV"
        wrhdu.header["TUNIT5"] = "keV"
        wrhdu.header["TUNIT6"] = "erg/s/cm**2"

        simputfile = prefix+"_simput.fits"

        wrhdu.writeto(simputfile, clobber=clobber)

    @parallel_root_only
    def write_h5_file(self, h5file):
        """
        Write an EventList to the HDF5 file given by *h5file*.
        """
        f = h5py.File(h5file, "w")

        p = f.create_group("parameters")
        p.create_dataset("exp_time", data=float(self.parameters["ExposureTime"]))
        area = self.parameters["Area"]
        if not isinstance(area, string_types):
            area = float(area)
        p.create_dataset("area", data=area)
        p.create_dataset("redshift", data=self.parameters["Redshift"])
        p.create_dataset("d_a", data=float(self.parameters["AngularDiameterDistance"]))
        if "ARF" in self.parameters:
            p.create_dataset("arf", data=self.parameters["ARF"])
        if "RMF" in self.parameters:
            p.create_dataset("rmf", data=self.parameters["RMF"])
        if "ChannelType" in self.parameters:
            p.create_dataset("channel_type", data=self.parameters["ChannelType"])
        if "Mission" in self.parameters:
            p.create_dataset("mission", data=self.parameters["Mission"])
        if "Telescope" in self.parameters:
            p.create_dataset("telescope", data=self.parameters["Telescope"])
        if "Instrument" in self.parameters:
            p.create_dataset("instrument", data=self.parameters["Instrument"])
        p.create_dataset("sky_center", data=self.parameters["sky_center"].d)
        p.create_dataset("pix_center", data=self.parameters["pix_center"])
        p.create_dataset("dtheta", data=float(self.parameters["dtheta"]))

        d = f.create_group("data")
        d.create_dataset("xpix", data=self["xpix"])
        d.create_dataset("ypix", data=self["ypix"])
        d.create_dataset("xsky", data=self["xsky"].d)
        d.create_dataset("ysky", data=self["ysky"].d)
        d.create_dataset("eobs", data=self["eobs"].d)
        if "PI" in self.events:
            d.create_dataset("pi", data=self.events["PI"])
        if "PHA" in self.events:
            d.create_dataset("pha", data=self.events["PHA"])

        f.close()

    @parallel_root_only
    def write_fits_image(self, imagefile, clobber=False,
                         emin=None, emax=None):
        r"""
        Generate a image by binning X-ray counts and write it to a FITS file.

        Parameters
        ----------
        imagefile : string
            The name of the image file to write.
        clobber : boolean, optional
            Set to True to overwrite a previous file.
        emin : float, optional
            The minimum energy of the photons to put in the image, in keV.
        emax : float, optional
            The maximum energy of the photons to put in the image, in keV.
        """
        if emin is None:
            mask_emin = np.ones(self.num_events, dtype='bool')
        else:
            mask_emin = self["eobs"].d > emin
        if emax is None:
            mask_emax = np.ones(self.num_events, dtype='bool')
        else:
            mask_emax = self["eobs"].d < emax

        mask = np.logical_and(mask_emin, mask_emax)

        nx = int(2*self.parameters["pix_center"][0]-1.)
        ny = int(2*self.parameters["pix_center"][1]-1.)

        xbins = np.linspace(0.5, float(nx)+0.5, nx+1, endpoint=True)
        ybins = np.linspace(0.5, float(ny)+0.5, ny+1, endpoint=True)

        H, xedges, yedges = np.histogram2d(self["xpix"][mask],
                                           self["ypix"][mask],
                                           bins=[xbins,ybins])

        hdu = _astropy.pyfits.PrimaryHDU(H.T)

        hdu.header["MTYPE1"] = "EQPOS"
        hdu.header["MFORM1"] = "RA,DEC"
        hdu.header["CTYPE1"] = "RA---TAN"
        hdu.header["CTYPE2"] = "DEC--TAN"
        hdu.header["CRPIX1"] = 0.5*(nx+1)
        hdu.header["CRPIX2"] = 0.5*(nx+1)
        hdu.header["CRVAL1"] = float(self.parameters["sky_center"][0])
        hdu.header["CRVAL2"] = float(self.parameters["sky_center"][1])
        hdu.header["CUNIT1"] = "deg"
        hdu.header["CUNIT2"] = "deg"
        hdu.header["CDELT1"] = -float(self.parameters["dtheta"])
        hdu.header["CDELT2"] = float(self.parameters["dtheta"])
        hdu.header["EXPOSURE"] = float(self.parameters["ExposureTime"])

        hdu.writeto(imagefile, clobber=clobber)

    @parallel_root_only
    def write_spectrum(self, specfile, bin_type="channel", emin=0.1,
                       emax=10.0, nchan=2000, clobber=False, energy_bins=False):
        r"""
        Bin event energies into a spectrum and write it to a FITS binary table. Can bin
        on energy or channel. In that case, the spectral binning will be determined by 
        the RMF binning.

        Parameters
        ----------
        specfile : string
            The name of the FITS file to be written.
        bin_type : string, optional
            Bin on "energy" or "channel". If an RMF is detected, channel information will be
            imported from it. 
        emin : float, optional
            The minimum energy of the spectral bins in keV. Only used if binning without an RMF.
        emax : float, optional
            The maximum energy of the spectral bins in keV. Only used if binning without an RMF.
        nchan : integer, optional
            The number of channels. Only used if binning without an RMF.
        energy_bins : boolean, optional
            Bin on energy or channel. Deprecated in favor of *bin_type*. 
        """
        if energy_bins:
            bin_type = "energy"
            warnings.warn("The energy_bins keyword is deprecated. Please use "
                          "the bin_type keyword instead. Setting bin_type == 'energy'.")
        pyfits = _astropy.pyfits
        if bin_type == "channel" and "ChannelType" in self.parameters:
            spectype = self.parameters["ChannelType"]
            f = pyfits.open(self.parameters["RMF"])
            nchan = int(f["EBOUNDS"].header["DETCHANS"])
            num = 0
            if "MATRIX" in f:
                mat_key = "MATRIX"
            elif "SPECRESP MATRIX" in f:
                mat_key = "SPECRESP MATRIX"
            for i in range(1,len(f[mat_key].columns)+1):
                if f[mat_key].header["TTYPE%d" % i] == "F_CHAN":
                    num = i
                    break
            if num > 0:
                tlmin = "TLMIN%d" % num
                cmin = int(f[mat_key].header[tlmin])
            else:
                mylog.warning("Cannot determine minimum allowed value for channel. " +
                              "Setting to 0, which may be wrong.")
                cmin = 0
            f.close()
            minlength = nchan
            if cmin == 1: minlength += 1
            spec = np.bincount(self[spectype],minlength=minlength)
            if cmin == 1: spec = spec[1:]
            bins = (np.arange(nchan)+cmin).astype("int32")
        else:
            espec = self["eobs"].d
            erange = (emin, emax)
            spec, ee = np.histogram(espec, bins=nchan, range=erange)
            if bin_type == "energy":
                bins = 0.5*(ee[1:]+ee[:-1])
                spectype = "energy"
            else:
                mylog.info("Events haven't been convolved with an RMF, so assuming "
                           "a perfect response and %d PI channels." % nchan)
                bins = (np.arange(nchan)+1).astype("int32")
                spectype = "pi"

        col1 = pyfits.Column(name='CHANNEL', format='1J', array=bins)
        col2 = pyfits.Column(name=spectype.upper(), format='1D', array=bins.astype("float64"))
        col3 = pyfits.Column(name='COUNTS', format='1J', array=spec.astype("int32"))
        col4 = pyfits.Column(name='COUNT_RATE', format='1D', array=spec/float(self.parameters["ExposureTime"]))

        coldefs = pyfits.ColDefs([col1, col2, col3, col4])

        tbhdu = pyfits.BinTableHDU.from_columns(coldefs)
        tbhdu.update_ext_name("SPECTRUM")

        tbhdu.header["DETCHANS"] = spec.shape[0]
        tbhdu.header["TOTCTS"] = spec.sum()
        tbhdu.header["EXPOSURE"] = float(self.parameters["ExposureTime"])
        tbhdu.header["LIVETIME"] = float(self.parameters["ExposureTime"])
        tbhdu.header["CONTENT"] = spectype
        tbhdu.header["HDUCLASS"] = "OGIP"
        tbhdu.header["HDUCLAS1"] = "SPECTRUM"
        tbhdu.header["HDUCLAS2"] = "TOTAL"
        tbhdu.header["HDUCLAS3"] = "TYPE:I"
        tbhdu.header["HDUCLAS4"] = "COUNT"
        tbhdu.header["HDUVERS"] = "1.1.0"
        tbhdu.header["HDUVERS1"] = "1.1.0"
        tbhdu.header["CHANTYPE"] = spectype
        tbhdu.header["BACKFILE"] = "none"
        tbhdu.header["CORRFILE"] = "none"
        tbhdu.header["POISSERR"] = True
        if "RMF" in self.parameters:
            tbhdu.header["RESPFILE"] = self.parameters["RMF"]
        else:
            tbhdu.header["RESPFILE"] = "none"
        if "ARF" in self.parameters:
            tbhdu.header["ANCRFILE"] = self.parameters["ARF"]
        else:
            tbhdu.header["ANCRFILE"] = "none"
        if "Mission" in self.parameters:
            tbhdu.header["MISSION"] = self.parameters["Mission"]
        else:
            tbhdu.header["MISSION"] = "none"
        if "Telescope" in self.parameters:
            tbhdu.header["TELESCOP"] = self.parameters["Telescope"]
        else:
            tbhdu.header["TELESCOP"] = "none"
        if "Instrument" in self.parameters:
            tbhdu.header["INSTRUME"] = self.parameters["Instrument"]
        else:
            tbhdu.header["INSTRUME"] = "none"
        tbhdu.header["AREASCAL"] = 1.0
        tbhdu.header["CORRSCAL"] = 0.0
        tbhdu.header["BACKSCAL"] = 1.0

        hdulist = pyfits.HDUList([pyfits.PrimaryHDU(), tbhdu])

        hdulist.writeto(specfile, clobber=clobber)

def merge_files(input_files, output_file, clobber=False,
                add_exposure_times=False):
    r"""
    Helper function for merging PhotonList or EventList HDF5 files.

    Parameters
    ----------
    input_files : list of strings
        List of filenames that will be merged together.
    output_file : string
        Name of the merged file to be outputted.
    clobber : boolean, default False
        If a the output file already exists, set this to True to
        overwrite it.
    add_exposure_times : boolean, default False
        If set to True, exposure times will be added together. Otherwise,
        the exposure times of all of the files must be the same.

    Examples
    --------
    >>> from yt.analysis_modules.photon_simulator.api import merge_files
    >>> merge_files(["events_0.h5","events_1.h5","events_3.h5"], "events.h5",
    ...             clobber=True, add_exposure_times=True)

    Notes
    -----
    Currently, to merge files it is mandated that all of the parameters have the
    same values, with the possible exception of the exposure time parameter "exp_time"
    if add_exposure_times=False.
    """
    if os.path.exists(output_file) and not clobber:
        raise IOError("Cannot overwrite existing file %s. " % output_file +
                      "If you want to do this, set clobber=True.")

    f_in = h5py.File(input_files[0], "r")
    f_out = h5py.File(output_file, "w")

    exp_time_key = ""
    p_out = f_out.create_group("parameters")
    for key, param in f_in["parameters"].items():
        if key.endswith("exp_time"):
            exp_time_key = key
        else:
            p_out[key] = param.value

    skip = [exp_time_key] if add_exposure_times else []
    for fn in input_files[1:]:
        f = h5py.File(fn, "r")
        validate_parameters(f_in["parameters"], f["parameters"], skip=skip)
        f.close()

    f_in.close()

    data = defaultdict(list)
    tot_exp_time = 0.0

    for i, fn in enumerate(input_files):
        f = h5py.File(fn, "r")
        if add_exposure_times:
            tot_exp_time += f["/parameters"][exp_time_key].value
        elif i == 0:
            tot_exp_time = f["/parameters"][exp_time_key].value
        for key in f["/data"]:
            data[key].append(f["/data"][key][:])
        f.close()

    p_out["exp_time"] = tot_exp_time

    d = f_out.create_group("data")
    for k in data:
        d.create_dataset(k, data=np.concatenate(data[k]))

    f_out.close()

def convert_old_file(input_file, output_file, clobber=False):
    r"""
    Helper function for converting old PhotonList or EventList HDF5
    files (pre yt v3.3) to their new versions.

    Parameters
    ----------
    input_file : list of strings
        The filename of the old-versioned file to be converted.
    output_file : string
        Name of the new file to be outputted.
    clobber : boolean, default False
        If a the output file already exists, set this to True to
        overwrite it.

    Examples
    --------
    >>> from yt.analysis_modules.photon_simulator.api import convert_old_file
    >>> convert_old_file("photons_old.h5", "photons_new.h5", clobber=True)
    """
    if os.path.exists(output_file) and not clobber:
        raise IOError("Cannot overwrite existing file %s. " % output_file +
                      "If you want to do this, set clobber=True.")

    f_in = h5py.File(input_file, "r")

    if "num_photons" in f_in:
        params = ["fid_exp_time", "fid_area", "fid_d_a", "fid_redshift",
                  "dimension", "width", "hubble", "omega_matter",
                  "omega_lambda"]
        data = ["x", "y", "z", "dx", "vx", "vy", "vz", "energy", "num_photons"]
    elif "pix_center" in f_in:
        params = ["exp_time", "area", "redshift", "d_a", "arf",
                  "rmf", "channel_type", "mission", "telescope",
                  "instrument", "sky_center", "pix_center", "dtheta"]
        data = ["xsky", "ysky", "xpix", "ypix", "eobs", "pi", "pha"]

    f_out = h5py.File(output_file, "w")

    p = f_out.create_group("parameters")
    d = f_out.create_group("data")

    for key in params:
        if key in f_in:
            p.create_dataset(key, data=f_in[key].value)

    for key in data:
        if key in f_in:
            d.create_dataset(key, data=f_in[key].value)

    f_in.close()
    f_out.close()
