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Instrument Model

For each Observation (see Data Containers), a unique instrument model is specified. This includes the set of detectors, their properties, and other metadata about the overall telescope.

toast.instrument.Site

Bases: object

Site base class.

Parameters:

Name Type Description Default
name str

Site name

 required
uid int

Unique identifier. If not specified, constructed from a hash of the site name.

 None
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class Site(object):
    """Site base class.

    Args:
        name (str):  Site name
        uid (int):  Unique identifier.  If not specified, constructed from a hash
            of the site name.

    """

    def __init__(self, name, uid=None):
        self.name = name
        self.uid = uid
        if self.uid is None:
            self.uid = name_UID(self.name)

    def _position(self, times):
        raise NotImplementedError("Derived class must implement _position()")

    def position(self, times):
        """Get the site position in solar system barycentric cartesian vectors.

        Given timestamps in POSIX seconds since 1970 (UTC), return the position as
        solar system coordinates.

        Args:
            times (array):  The timestamps.

        Returns:
            (array):  The position vectors.

        """
        return self._position(times)

    def _velocity(self, times):
        raise NotImplementedError("Derived class must implement _velocity()")

    def velocity(self, times):
        """Get the site velocity in solar system barycentric cartesian vectors.

        Given timestamps in POSIX seconds since 1970 (UTC), return the velocity as
        quaternions in solar system barycentric coordinates.

        Args:
            times (array):  The timestamps.

        Returns:
            (array):  The velocity vectors.

        """
        return self._velocity(times)

    def position_velocity(self, times):
        """Get the site position and velocity.

        Convenience function to simultaneously return the position and velocity.

        Args:
            times (array):  The timestamps.

        Returns:
            (tuple):  The position and velocity arrays of vectors.

        """
        if hasattr(self, "_position_velocity"):
            return self._position_velocity(times)
        else:
            p = self._position(times)
            v = self._velocity(times)
        return (p, v)

    def __repr__(self):
        value = "<Site '{}' : uid = {}>".format(self.name, self.uid)
        return value

    def __eq__(self, other):
        if self.name != other.name:
            return False
        if self.uid != other.uid:
            return False
        return True

    def __ne__(self, other):
        return not self.__eq__(other)

name = name instance-attribute

uid = uid instance-attribute

__eq__(other)

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def __eq__(self, other):
    if self.name != other.name:
        return False
    if self.uid != other.uid:
        return False
    return True

__init__(name, uid=None)

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def __init__(self, name, uid=None):
    self.name = name
    self.uid = uid
    if self.uid is None:
        self.uid = name_UID(self.name)

__ne__(other)

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def __ne__(self, other):
    return not self.__eq__(other)

__repr__()

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def __repr__(self):
    value = "<Site '{}' : uid = {}>".format(self.name, self.uid)
    return value

_position(times)

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def _position(self, times):
    raise NotImplementedError("Derived class must implement _position()")

_velocity(times)

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def _velocity(self, times):
    raise NotImplementedError("Derived class must implement _velocity()")

position(times)

Get the site position in solar system barycentric cartesian vectors.

Given timestamps in POSIX seconds since 1970 (UTC), return the position as solar system coordinates.

Parameters:

Name Type Description Default
times array

The timestamps.

 required

Returns:

Type Description
array

The position vectors.
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def position(self, times):
    """Get the site position in solar system barycentric cartesian vectors.

    Given timestamps in POSIX seconds since 1970 (UTC), return the position as
    solar system coordinates.

    Args:
        times (array):  The timestamps.

    Returns:
        (array):  The position vectors.

    """
    return self._position(times)

position_velocity(times)

Get the site position and velocity.

Convenience function to simultaneously return the position and velocity.

Parameters:

Name Type Description Default
times array

The timestamps.

 required

Returns:

Type Description
tuple

The position and velocity arrays of vectors.
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def position_velocity(self, times):
    """Get the site position and velocity.

    Convenience function to simultaneously return the position and velocity.

    Args:
        times (array):  The timestamps.

    Returns:
        (tuple):  The position and velocity arrays of vectors.

    """
    if hasattr(self, "_position_velocity"):
        return self._position_velocity(times)
    else:
        p = self._position(times)
        v = self._velocity(times)
    return (p, v)

velocity(times)

Get the site velocity in solar system barycentric cartesian vectors.

Given timestamps in POSIX seconds since 1970 (UTC), return the velocity as quaternions in solar system barycentric coordinates.

Parameters:

Name Type Description Default
times array

The timestamps.

 required

Returns:

Type Description
array

The velocity vectors.
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def velocity(self, times):
    """Get the site velocity in solar system barycentric cartesian vectors.

    Given timestamps in POSIX seconds since 1970 (UTC), return the velocity as
    quaternions in solar system barycentric coordinates.

    Args:
        times (array):  The timestamps.

    Returns:
        (array):  The velocity vectors.

    """
    return self._velocity(times)

toast.instrument.GroundSite

Bases: Site

Site on the Earth.

This represents a fixed location on the Earth.

Parameters:

Name Type Description Default
name str

Site name

 required
lat Quantity

Site latitude.

 required
lon Quantity

Site longitude.

 required
alt Quantity

Site altitude.

 required
uid int

Unique identifier. If not specified, constructed from a hash of the site name.

 None
weather Weather

Weather information for this site.

 None
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class GroundSite(Site):
    """Site on the Earth.

    This represents a fixed location on the Earth.

    Args:
        name (str):  Site name
        lat (Quantity):  Site latitude.
        lon (Quantity):  Site longitude.
        alt (Quantity):  Site altitude.
        uid (int):  Unique identifier.  If not specified, constructed from a hash
            of the site name.
        weather (Weather):  Weather information for this site.
    """

    def __init__(self, name, lat, lon, alt, uid=None, weather=None):
        super().__init__(name, uid)
        self.earthloc = coord.EarthLocation.from_geodetic(lon, lat, height=alt)
        self.weather = weather

    def __repr__(self):
        value = "<GroundSite '{}' : uid = {}, lon = {}, lat = {}, alt = {}, weather = {}>".format(
            self.name,
            self.uid,
            self.earthloc.lon,
            self.earthloc.lat,
            self.earthloc.height,
            self.weather,
        )
        return value

    def __eq__(self, other):
        if self.name != other.name:
            return False
        if self.uid != other.uid:
            return False
        if not np.isclose(other.earthloc.lon, self.earthloc.lon):
            return False
        if not np.isclose(other.earthloc.lat, self.earthloc.lat):
            return False
        if not np.isclose(other.earthloc.height, self.earthloc.height):
            return False
        if self.weather != other.weather:
            return False
        return True

    def _position_velocity(self, times):
        # Compute data at 10 second intervals and interpolate.  If the timestamps are
        # more coarsely sampled than that, just compute those times directly.
        sparse_incr = 10.0
        do_interp = True
        if len(times) < 100 or (times[1] - times[0]) > sparse_incr:
            do_interp = False

        if do_interp:
            n_sparse = int((times[-1] - times[0]) / sparse_incr)
            sparse_times = np.linspace(times[0], times[-1], num=n_sparse, endpoint=True)
        else:
            n_sparse = len(times)
            sparse_times = times
        pos_x = np.zeros(n_sparse, np.float64)
        pos_y = np.zeros(n_sparse, np.float64)
        pos_z = np.zeros(n_sparse, np.float64)
        vel_x = np.zeros(n_sparse, np.float64)
        vel_y = np.zeros(n_sparse, np.float64)
        vel_z = np.zeros(n_sparse, np.float64)
        for i, t in enumerate(sparse_times):
            atime = astime.Time(t, format="unix")
            p, v = coord.get_body_barycentric_posvel("earth", atime)
            # FIXME:  apply translation from earth center to earth location.
            # itrs = self.earthloc.get_itrs(obstime)
            pm = p.xyz.to_value(u.kilometer)
            vm = v.xyz.to_value(u.kilometer / u.second)
            pos_x[i] = pm[0]
            pos_y[i] = pm[1]
            pos_z[i] = pm[2]
            vel_x[i] = vm[0]
            vel_y[i] = vm[1]
            vel_z[i] = vm[2]

        if do_interp:
            pos_x = np.interp(times, sparse_times, pos_x)
            pos_y = np.interp(times, sparse_times, pos_y)
            pos_z = np.interp(times, sparse_times, pos_z)
            vel_x = np.interp(times, sparse_times, vel_x)
            vel_y = np.interp(times, sparse_times, vel_y)
            vel_z = np.interp(times, sparse_times, vel_z)
        pos = np.stack([pos_x, pos_y, pos_z], axis=-1)
        vel = np.stack([vel_x, vel_y, vel_z], axis=-1)
        return pos, vel

    def _position(self, times):
        p, v = self._position_velocity(times)
        return p

    def _velocity(self, times):
        p, v = self._position_velocity(times)
        return v

earthloc = coord.EarthLocation.from_geodetic(lon, lat, height=alt) instance-attribute

weather = weather instance-attribute

__eq__(other)

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def __eq__(self, other):
    if self.name != other.name:
        return False
    if self.uid != other.uid:
        return False
    if not np.isclose(other.earthloc.lon, self.earthloc.lon):
        return False
    if not np.isclose(other.earthloc.lat, self.earthloc.lat):
        return False
    if not np.isclose(other.earthloc.height, self.earthloc.height):
        return False
    if self.weather != other.weather:
        return False
    return True

__init__(name, lat, lon, alt, uid=None, weather=None)

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def __init__(self, name, lat, lon, alt, uid=None, weather=None):
    super().__init__(name, uid)
    self.earthloc = coord.EarthLocation.from_geodetic(lon, lat, height=alt)
    self.weather = weather

__repr__()

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def __repr__(self):
    value = "<GroundSite '{}' : uid = {}, lon = {}, lat = {}, alt = {}, weather = {}>".format(
        self.name,
        self.uid,
        self.earthloc.lon,
        self.earthloc.lat,
        self.earthloc.height,
        self.weather,
    )
    return value

_position(times)

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def _position(self, times):
    p, v = self._position_velocity(times)
    return p

_position_velocity(times)

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def _position_velocity(self, times):
    # Compute data at 10 second intervals and interpolate.  If the timestamps are
    # more coarsely sampled than that, just compute those times directly.
    sparse_incr = 10.0
    do_interp = True
    if len(times) < 100 or (times[1] - times[0]) > sparse_incr:
        do_interp = False

    if do_interp:
        n_sparse = int((times[-1] - times[0]) / sparse_incr)
        sparse_times = np.linspace(times[0], times[-1], num=n_sparse, endpoint=True)
    else:
        n_sparse = len(times)
        sparse_times = times
    pos_x = np.zeros(n_sparse, np.float64)
    pos_y = np.zeros(n_sparse, np.float64)
    pos_z = np.zeros(n_sparse, np.float64)
    vel_x = np.zeros(n_sparse, np.float64)
    vel_y = np.zeros(n_sparse, np.float64)
    vel_z = np.zeros(n_sparse, np.float64)
    for i, t in enumerate(sparse_times):
        atime = astime.Time(t, format="unix")
        p, v = coord.get_body_barycentric_posvel("earth", atime)
        # FIXME:  apply translation from earth center to earth location.
        # itrs = self.earthloc.get_itrs(obstime)
        pm = p.xyz.to_value(u.kilometer)
        vm = v.xyz.to_value(u.kilometer / u.second)
        pos_x[i] = pm[0]
        pos_y[i] = pm[1]
        pos_z[i] = pm[2]
        vel_x[i] = vm[0]
        vel_y[i] = vm[1]
        vel_z[i] = vm[2]

    if do_interp:
        pos_x = np.interp(times, sparse_times, pos_x)
        pos_y = np.interp(times, sparse_times, pos_y)
        pos_z = np.interp(times, sparse_times, pos_z)
        vel_x = np.interp(times, sparse_times, vel_x)
        vel_y = np.interp(times, sparse_times, vel_y)
        vel_z = np.interp(times, sparse_times, vel_z)
    pos = np.stack([pos_x, pos_y, pos_z], axis=-1)
    vel = np.stack([vel_x, vel_y, vel_z], axis=-1)
    return pos, vel

_velocity(times)

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def _velocity(self, times):
    p, v = self._position_velocity(times)
    return v

toast.instrument.SpaceSite

Bases: Site

Site with no atmosphere.

This represents a location beyond the Earth's atmosphere. In practice, this should be sub-classed for real satellite experiments.

Parameters:

Name Type Description Default
name str

Site name

 required
uid int

Unique identifier. If not specified, constructed from a hash of the site name.

 None
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class SpaceSite(Site):
    """Site with no atmosphere.

    This represents a location beyond the Earth's atmosphere.  In practice, this
    should be sub-classed for real satellite experiments.

    Args:
        name (str):  Site name
        uid (int):  Unique identifier.  If not specified, constructed from a hash
            of the site name.

    """

    def __init__(self, name, uid=None):
        super().__init__(name, uid)

    def __repr__(self):
        value = "<SpaceSite '{}' : uid = {}>".format(self.name, self.uid)
        return value

    def _position_velocity(self, times):
        # Compute data at 10 minute intervals and interpolate.  If the timestamps are
        # more coarsely sampled than that, just compute those times directly.
        sparse_incr = 600.0
        do_interp = True
        if len(times) < 100 or (times[1] - times[0]) > sparse_incr:
            do_interp = False

        if do_interp:
            n_sparse = 1 + int((times[-1] - times[0]) / sparse_incr)
            sparse_times = np.linspace(times[0], times[-1], num=n_sparse, endpoint=True)
        else:
            n_sparse = len(times)
            sparse_times = times
        pos_x = np.zeros(n_sparse, np.float64)
        pos_y = np.zeros(n_sparse, np.float64)
        pos_z = np.zeros(n_sparse, np.float64)
        vel_x = np.zeros(n_sparse, np.float64)
        vel_y = np.zeros(n_sparse, np.float64)
        vel_z = np.zeros(n_sparse, np.float64)
        for i, t in enumerate(sparse_times):
            atime = astime.Time(t, format="unix")
            # Get the satellite position and velocity in the equatorial frame (ICRS)
            p, v = coord.get_body_barycentric_posvel("earth", atime)
            # FIXME:  apply translation from earth center to L2.
            pm = p.xyz.to_value(u.kilometer)
            vm = v.xyz.to_value(u.kilometer / u.second)
            pos_x[i] = pm[0]
            pos_y[i] = pm[1]
            pos_z[i] = pm[2]
            vel_x[i] = vm[0]
            vel_y[i] = vm[1]
            vel_z[i] = vm[2]

        if do_interp:
            pos_x = np.interp(times, sparse_times, pos_x)
            pos_y = np.interp(times, sparse_times, pos_y)
            pos_z = np.interp(times, sparse_times, pos_z)
            vel_x = np.interp(times, sparse_times, vel_x)
            vel_y = np.interp(times, sparse_times, vel_y)
            vel_z = np.interp(times, sparse_times, vel_z)
        pos = np.stack([pos_x, pos_y, pos_z], axis=-1)
        vel = np.stack([vel_x, vel_y, vel_z], axis=-1)
        return pos, vel

    def _position(self, times):
        p, v = self._position_velocity(times)
        return p

    def _velocity(self, times):
        p, v = self._position_velocity(times)
        return v

__init__(name, uid=None)

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def __init__(self, name, uid=None):
    super().__init__(name, uid)

__repr__()

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def __repr__(self):
    value = "<SpaceSite '{}' : uid = {}>".format(self.name, self.uid)
    return value

_position(times)

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def _position(self, times):
    p, v = self._position_velocity(times)
    return p

_position_velocity(times)

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def _position_velocity(self, times):
    # Compute data at 10 minute intervals and interpolate.  If the timestamps are
    # more coarsely sampled than that, just compute those times directly.
    sparse_incr = 600.0
    do_interp = True
    if len(times) < 100 or (times[1] - times[0]) > sparse_incr:
        do_interp = False

    if do_interp:
        n_sparse = 1 + int((times[-1] - times[0]) / sparse_incr)
        sparse_times = np.linspace(times[0], times[-1], num=n_sparse, endpoint=True)
    else:
        n_sparse = len(times)
        sparse_times = times
    pos_x = np.zeros(n_sparse, np.float64)
    pos_y = np.zeros(n_sparse, np.float64)
    pos_z = np.zeros(n_sparse, np.float64)
    vel_x = np.zeros(n_sparse, np.float64)
    vel_y = np.zeros(n_sparse, np.float64)
    vel_z = np.zeros(n_sparse, np.float64)
    for i, t in enumerate(sparse_times):
        atime = astime.Time(t, format="unix")
        # Get the satellite position and velocity in the equatorial frame (ICRS)
        p, v = coord.get_body_barycentric_posvel("earth", atime)
        # FIXME:  apply translation from earth center to L2.
        pm = p.xyz.to_value(u.kilometer)
        vm = v.xyz.to_value(u.kilometer / u.second)
        pos_x[i] = pm[0]
        pos_y[i] = pm[1]
        pos_z[i] = pm[2]
        vel_x[i] = vm[0]
        vel_y[i] = vm[1]
        vel_z[i] = vm[2]

    if do_interp:
        pos_x = np.interp(times, sparse_times, pos_x)
        pos_y = np.interp(times, sparse_times, pos_y)
        pos_z = np.interp(times, sparse_times, pos_z)
        vel_x = np.interp(times, sparse_times, vel_x)
        vel_y = np.interp(times, sparse_times, vel_y)
        vel_z = np.interp(times, sparse_times, vel_z)
    pos = np.stack([pos_x, pos_y, pos_z], axis=-1)
    vel = np.stack([vel_x, vel_y, vel_z], axis=-1)
    return pos, vel

_velocity(times)

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def _velocity(self, times):
    p, v = self._position_velocity(times)
    return v

toast.instrument.Bandpass

Bases: object

Class that contains the bandpass information for an entire focalplane.

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class Bandpass(object):
    """Class that contains the bandpass information for an entire focalplane."""

    @function_timer
    def __init__(self, bandcenters, bandwidths, nstep=101):
        """All units in GHz

        Args :
            bandcenters(dict) : Dictionary of bandpass centers
            bandwidths(dict) : Dictionary of bandpass widths
            nstep(int) : Number of interplation steps to use in `convolve()`
        """
        self.nstep = nstep
        self.dets = []
        self._fmin = {}
        self._fmax = {}
        for name in bandcenters:
            self.dets.append(name)
            center = bandcenters[name]
            width = bandwidths[name]
            self._fmin[name] = center - 0.5 * width
            self._fmax[name] = center + 0.5 * width
        # The interpolated bandpasses will be cached as needed
        self._fmin_tot = None
        self._fmax_tot = None
        self._freqs = {}
        self._bandpass = {}
        self._kcmb2jysr = {}
        self._kcmb2krj = {}
        self._kcmb2w = {}

    @function_timer
    def get_range(self, det=None):
        """Return the maximum range of frequencies needed for convolution."""
        if det is not None:
            return self._fmin[det], self._fmax[det]
        elif self._fmin_tot is None:
            self._fmin_tot = min(self._fmin.values())
            self._fmax_tot = max(self._fmax.values())
        return self._fmin_tot, self._fmax_tot

    @function_timer
    def center_frequency(self, det, alpha=-1):
        """Return the effective central frequency for a given spectral index"""

        # Which delta function bandpass would produce the same flux density
        freqs = self.freqs(det)
        if alpha == 0:
            # The equation is singular at alpha == 0. Evaluate it on both sides
            # and return the average
            delta = 1e-6
            alpha1 = alpha - delta
            eff1 = self.convolve(det, freqs, freqs.to_value(u.Hz) ** alpha1) ** (
                1 / alpha1
            )
            alpha2 = alpha + delta
            eff2 = self.convolve(det, freqs, freqs.to_value(u.Hz) ** alpha2) ** (
                1 / alpha2
            )
            eff = 0.5 * (eff1 + eff2)
        else:
            # Very simple closed form
            eff = self.convolve(det, freqs, freqs.to_value(u.Hz) ** alpha) ** (
                1 / alpha
            )

        return eff * u.Hz

    @function_timer
    def optical_loading(self, det, T):
        """Return the optical loading of a blackbody source.
        Assumes a diffraction-limited, single-moded polarimeter
        and perfect optical efficiency
        arXiv:1806.04316

        Args:
            det(str) : detector name
            T(float) : Source temperature in Kelvin

        Returns:
            (float) : The optical loading in Watts
        """

        bandpass = self.bandpass(det)
        # Normalize the bandpass to peak at 1
        bandpass = bandpass / np.amax(bandpass)
        freqs = self.freqs(det).to_value(u.Hz)

        # Power spectral density
        S = h * freqs / (np.exp(h * freqs / k / T) - 1)

        # Integrate over frequency to get power
        power = integrate_simpson(freqs, S * bandpass)

        return power

    @function_timer
    def _get_unit_conversion_coefficients(self, det):
        """Compute and cache the unit conversion coefficients for one detector"""

        if (
            det not in self._kcmb2jysr
            or det not in self._kcmb2krj
            or det not in self._kcmb2w
        ):
            # The calculation is a copy from the Hildebrandt and Macias-Perez IDL module for Planck

            nu_cmb = k * TCMB / h
            alpha = 2 * k**3 * TCMB**2 / h**2 / c**2

            cfreq = self.center_frequency(det).to_value(u.Hz)
            freqs = self.freqs(det).to_value(u.Hz)
            bandpass = self.bandpass(det)

            x = freqs / nu_cmb
            db_dt = alpha * x**4 * np.exp(x) / (np.exp(x) - 1) ** 2
            db_dt_rj = 2 * freqs**2 * k / c**2

            self._kcmb2jysr[det] = (
                1e26
                * integrate_simpson(freqs, db_dt * bandpass)
                / integrate_simpson(freqs, cfreq / freqs * bandpass)
            )
            self._kcmb2krj[det] = integrate_simpson(
                freqs, db_dt * bandpass
            ) / integrate_simpson(freqs, db_dt_rj * bandpass)

            # K_CMB->W conversion is from the BoloCalc paper, arXiv:1806.04316
            bandpass = bandpass / np.amax(bandpass)
            self._kcmb2w[det] = integrate_simpson(
                freqs,
                k * (x / (np.exp(x) - 1)) ** 2 * np.exp(x) * bandpass,
            )

        return

    @function_timer
    def freqs(self, det):
        if det not in self._freqs:
            fmin = self._fmin[det].to_value(u.Hz)
            fmax = self._fmax[det].to_value(u.Hz)
            self._freqs[det] = np.linspace(fmin, fmax, self.nstep) * u.Hz
        return self._freqs[det]

    @function_timer
    def bandpass(self, det):
        if det not in self._bandpass:
            # Normalize and interpolate the bandpass
            freqs = self.freqs(det)
            try:
                # If we have a tabulated bandpass, interpolate it
                self._bandpass[det] = np.interp(
                    freqs.to_value(u.Hz),
                    self._bins[det].to_value(u.Hz),
                    self._values[det],
                )
            except AttributeError:
                self._bandpass[det] = np.ones(self.nstep)

            # norm = simpson(self.bandpass[det], x=self.freqs[det])
            norm = integrate_simpson(freqs.to_value(u.Hz), self._bandpass[det])
            if norm == 0:
                raise RuntimeError("Bandpass cannot be normalized")
            self._bandpass[det] /= norm

        return self._bandpass[det]

    @function_timer
    def kcmb2jysr(self, det):
        """Return the unit conversion between K_CMB and Jy/sr"""
        self._get_unit_conversion_coefficients(det)
        return self._kcmb2jysr[det]

    @function_timer
    def kcmb2krj(self, det):
        """Return the unit conversion between K_CMB and K_RJ"""
        self._get_unit_conversion_coefficients(det)
        return self._kcmb2krj[det]

    @function_timer
    def kcmb2w(self, det):
        """Return the unit conversion between K_CMB and W"""
        self._get_unit_conversion_coefficients(det)
        return self._kcmb2w[det]

    @function_timer
    def convolve(self, det, freqs, spectrum, rj=False):
        """Convolve the provided spectrum with the detector bandpass

        Args:
            det(str):  Detector name
            freqs(array of floats):  Spectral bin locations
            spectrum(array of floats):  Spectral bin values
            rj(bool):  Input spectrum is in Rayleigh-Jeans units and
                should be converted into thermal units for convolution

        Returns:
            (array):  The bandpass-convolved spectrum
        """
        freqs_det = self.freqs(det)
        bandpass_det = self.bandpass(det)

        # Interpolate spectrum values to bandpass frequencies
        spectrum_det = np.interp(
            freqs_det.to_value(u.Hz), freqs.to_value(u.Hz), spectrum
        )

        if rj:
            # From brightness to thermodynamic units
            x = h * freqs_det.to_value(u.Hz) / k / TCMB
            rj2cmb = (x / (np.exp(x / 2) - np.exp(-x / 2))) ** -2
            spectrum_det *= rj2cmb

        # Average across the bandpass
        convolved = integrate_simpson(
            freqs_det.to_value(u.Hz), spectrum_det * bandpass_det
        )

        return convolved

_bandpass = {} instance-attribute

_fmax = {} instance-attribute

_fmax_tot = None instance-attribute

_fmin = {} instance-attribute

_fmin_tot = None instance-attribute

_freqs = {} instance-attribute

_kcmb2jysr = {} instance-attribute

_kcmb2krj = {} instance-attribute

_kcmb2w = {} instance-attribute

dets = [] instance-attribute

nstep = nstep instance-attribute

__init__(bandcenters, bandwidths, nstep=101)

All units in GHz

Args

bandcenters(dict) : Dictionary of bandpass centers bandwidths(dict) : Dictionary of bandpass widths nstep(int) : Number of interplation steps to use in convolve()

Source code in toast/instrument.py 
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@function_timer
def __init__(self, bandcenters, bandwidths, nstep=101):
    """All units in GHz

    Args :
        bandcenters(dict) : Dictionary of bandpass centers
        bandwidths(dict) : Dictionary of bandpass widths
        nstep(int) : Number of interplation steps to use in `convolve()`
    """
    self.nstep = nstep
    self.dets = []
    self._fmin = {}
    self._fmax = {}
    for name in bandcenters:
        self.dets.append(name)
        center = bandcenters[name]
        width = bandwidths[name]
        self._fmin[name] = center - 0.5 * width
        self._fmax[name] = center + 0.5 * width
    # The interpolated bandpasses will be cached as needed
    self._fmin_tot = None
    self._fmax_tot = None
    self._freqs = {}
    self._bandpass = {}
    self._kcmb2jysr = {}
    self._kcmb2krj = {}
    self._kcmb2w = {}

_get_unit_conversion_coefficients(det)

Compute and cache the unit conversion coefficients for one detector

Source code in toast/instrument.py 
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@function_timer
def _get_unit_conversion_coefficients(self, det):
    """Compute and cache the unit conversion coefficients for one detector"""

    if (
        det not in self._kcmb2jysr
        or det not in self._kcmb2krj
        or det not in self._kcmb2w
    ):
        # The calculation is a copy from the Hildebrandt and Macias-Perez IDL module for Planck

        nu_cmb = k * TCMB / h
        alpha = 2 * k**3 * TCMB**2 / h**2 / c**2

        cfreq = self.center_frequency(det).to_value(u.Hz)
        freqs = self.freqs(det).to_value(u.Hz)
        bandpass = self.bandpass(det)

        x = freqs / nu_cmb
        db_dt = alpha * x**4 * np.exp(x) / (np.exp(x) - 1) ** 2
        db_dt_rj = 2 * freqs**2 * k / c**2

        self._kcmb2jysr[det] = (
            1e26
            * integrate_simpson(freqs, db_dt * bandpass)
            / integrate_simpson(freqs, cfreq / freqs * bandpass)
        )
        self._kcmb2krj[det] = integrate_simpson(
            freqs, db_dt * bandpass
        ) / integrate_simpson(freqs, db_dt_rj * bandpass)

        # K_CMB->W conversion is from the BoloCalc paper, arXiv:1806.04316
        bandpass = bandpass / np.amax(bandpass)
        self._kcmb2w[det] = integrate_simpson(
            freqs,
            k * (x / (np.exp(x) - 1)) ** 2 * np.exp(x) * bandpass,
        )

    return

bandpass(det)

Source code in toast/instrument.py 
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@function_timer
def bandpass(self, det):
    if det not in self._bandpass:
        # Normalize and interpolate the bandpass
        freqs = self.freqs(det)
        try:
            # If we have a tabulated bandpass, interpolate it
            self._bandpass[det] = np.interp(
                freqs.to_value(u.Hz),
                self._bins[det].to_value(u.Hz),
                self._values[det],
            )
        except AttributeError:
            self._bandpass[det] = np.ones(self.nstep)

        # norm = simpson(self.bandpass[det], x=self.freqs[det])
        norm = integrate_simpson(freqs.to_value(u.Hz), self._bandpass[det])
        if norm == 0:
            raise RuntimeError("Bandpass cannot be normalized")
        self._bandpass[det] /= norm

    return self._bandpass[det]

center_frequency(det, alpha=-1)

Return the effective central frequency for a given spectral index

Source code in toast/instrument.py 
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@function_timer
def center_frequency(self, det, alpha=-1):
    """Return the effective central frequency for a given spectral index"""

    # Which delta function bandpass would produce the same flux density
    freqs = self.freqs(det)
    if alpha == 0:
        # The equation is singular at alpha == 0. Evaluate it on both sides
        # and return the average
        delta = 1e-6
        alpha1 = alpha - delta
        eff1 = self.convolve(det, freqs, freqs.to_value(u.Hz) ** alpha1) ** (
            1 / alpha1
        )
        alpha2 = alpha + delta
        eff2 = self.convolve(det, freqs, freqs.to_value(u.Hz) ** alpha2) ** (
            1 / alpha2
        )
        eff = 0.5 * (eff1 + eff2)
    else:
        # Very simple closed form
        eff = self.convolve(det, freqs, freqs.to_value(u.Hz) ** alpha) ** (
            1 / alpha
        )

    return eff * u.Hz

convolve(det, freqs, spectrum, rj=False)

Convolve the provided spectrum with the detector bandpass

Parameters:

Name Type Description Default
det(str)

Detector name

 required
freqs(array of floats

Spectral bin locations

 required
spectrum(array of floats

Spectral bin values

 required
rj(bool)

Input spectrum is in Rayleigh-Jeans units and should be converted into thermal units for convolution

 required

Returns:

Type Description
array

The bandpass-convolved spectrum
Source code in toast/instrument.py 
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@function_timer
def convolve(self, det, freqs, spectrum, rj=False):
    """Convolve the provided spectrum with the detector bandpass

    Args:
        det(str):  Detector name
        freqs(array of floats):  Spectral bin locations
        spectrum(array of floats):  Spectral bin values
        rj(bool):  Input spectrum is in Rayleigh-Jeans units and
            should be converted into thermal units for convolution

    Returns:
        (array):  The bandpass-convolved spectrum
    """
    freqs_det = self.freqs(det)
    bandpass_det = self.bandpass(det)

    # Interpolate spectrum values to bandpass frequencies
    spectrum_det = np.interp(
        freqs_det.to_value(u.Hz), freqs.to_value(u.Hz), spectrum
    )

    if rj:
        # From brightness to thermodynamic units
        x = h * freqs_det.to_value(u.Hz) / k / TCMB
        rj2cmb = (x / (np.exp(x / 2) - np.exp(-x / 2))) ** -2
        spectrum_det *= rj2cmb

    # Average across the bandpass
    convolved = integrate_simpson(
        freqs_det.to_value(u.Hz), spectrum_det * bandpass_det
    )

    return convolved

freqs(det)

Source code in toast/instrument.py 
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@function_timer
def freqs(self, det):
    if det not in self._freqs:
        fmin = self._fmin[det].to_value(u.Hz)
        fmax = self._fmax[det].to_value(u.Hz)
        self._freqs[det] = np.linspace(fmin, fmax, self.nstep) * u.Hz
    return self._freqs[det]

get_range(det=None)

Return the maximum range of frequencies needed for convolution.

Source code in toast/instrument.py 
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@function_timer
def get_range(self, det=None):
    """Return the maximum range of frequencies needed for convolution."""
    if det is not None:
        return self._fmin[det], self._fmax[det]
    elif self._fmin_tot is None:
        self._fmin_tot = min(self._fmin.values())
        self._fmax_tot = max(self._fmax.values())
    return self._fmin_tot, self._fmax_tot

kcmb2jysr(det)

Return the unit conversion between K_CMB and Jy/sr

Source code in toast/instrument.py 
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@function_timer
def kcmb2jysr(self, det):
    """Return the unit conversion between K_CMB and Jy/sr"""
    self._get_unit_conversion_coefficients(det)
    return self._kcmb2jysr[det]

kcmb2krj(det)

Return the unit conversion between K_CMB and K_RJ

Source code in toast/instrument.py 
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@function_timer
def kcmb2krj(self, det):
    """Return the unit conversion between K_CMB and K_RJ"""
    self._get_unit_conversion_coefficients(det)
    return self._kcmb2krj[det]

kcmb2w(det)

Return the unit conversion between K_CMB and W

Source code in toast/instrument.py 
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@function_timer
def kcmb2w(self, det):
    """Return the unit conversion between K_CMB and W"""
    self._get_unit_conversion_coefficients(det)
    return self._kcmb2w[det]

optical_loading(det, T)

Return the optical loading of a blackbody source. Assumes a diffraction-limited, single-moded polarimeter and perfect optical efficiency arXiv:1806.04316

Parameters:

Name Type Description Default
det(str)

detector name

 required
T(float)

Source temperature in Kelvin

 required

Returns:

Type Description

(float) : The optical loading in Watts
Source code in toast/instrument.py 
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@function_timer
def optical_loading(self, det, T):
    """Return the optical loading of a blackbody source.
    Assumes a diffraction-limited, single-moded polarimeter
    and perfect optical efficiency
    arXiv:1806.04316

    Args:
        det(str) : detector name
        T(float) : Source temperature in Kelvin

    Returns:
        (float) : The optical loading in Watts
    """

    bandpass = self.bandpass(det)
    # Normalize the bandpass to peak at 1
    bandpass = bandpass / np.amax(bandpass)
    freqs = self.freqs(det).to_value(u.Hz)

    # Power spectral density
    S = h * freqs / (np.exp(h * freqs / k / T) - 1)

    # Integrate over frequency to get power
    power = integrate_simpson(freqs, S * bandpass)

    return power

toast.instrument.Focalplane

Bases: object

Class representing the focalplane for one observation.

The detector_data Table may store arbitrary columns, but several are required. They include:

"name":  The detector name.
"quat":  Each row should be a 4-element numpy array.
"gamma":  If using a half wave plate, we need the rotation angle of the
    detector polarization orientation from the focalplane frame X-axis.

Some columns are optional:

"uid":  Unique integer ID for each detector.  Computed from detector name if
    not specified.
"pol_angle":  Quantity to specify the polarization angle.  Default assumes
    the polarization sensitive direction is aligned with the detector
    quaternion rotation.  Computed if not specified.
"pol_leakage":  Float value "epsilon" between 0-1.  Set to zero by default.
"pol_efficiency":  Float value "eta" = (1 - epsilon) / (1 + epsilon).  Set
    to one by default.
"fwhm":  Quantity with the nominal beam FWHM.  Used for plotting and for
    smoothing of simulated sky signal with PySM.
"bandcenter":  Quantity for the band center.  Used for bandpass integration
    with PySM simulations.
"bandwidth":  Quantity for width of the band.  Used for bandpass integration
    with PySM simulations.
"psd_net":  The detector sensitivity.  Quantity used to create a synthetic
    noise model with the DefaultNoiseModel operator.
"psd_fknee":  Quantity used to create a synthetic noise model with the
    DefaultNoiseModel operator.
"psd_fmin":  Quantity used to create a synthetic noise model with the
    DefaultNoiseModel operator.
"psd_alpha":  Quantity used to create a synthetic noise model with the
    DefaultNoiseModel operator.
"elevation_noise_a" and "elevation_noise_c":  Parameters of elevation scaling
    noise model: PSD_{out} = PSD_{ref} * (a / sin(el) + c)^2.  Only applicable
    to ground data.
"pwv_a0", "pwv_a1" and "pwv_a2":  quadratic fit of the NET modulation by
    PWV.  Only applicable to ground data.

Parameters:

Name Type Description Default
detector_data QTable

Table of detector properties.

 None
field_of_view Quantity

Angular diameter of the focal plane. Used to increase the effective size of the focalplane when simulating atmosphere, etc. Will be calculated from the detector offsets by default.

 None
sample_rate Quantity

The common (nominal) sample rate for all detectors.

 None
thinfp int

Only sample the detectors in the file.

 None
Source code in toast/instrument.py 
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class Focalplane(object):
    """Class representing the focalplane for one observation.

    The detector_data Table may store arbitrary columns, but several are required.
    They include:

        "name":  The detector name.
        "quat":  Each row should be a 4-element numpy array.
        "gamma":  If using a half wave plate, we need the rotation angle of the
            detector polarization orientation from the focalplane frame X-axis.

    Some columns are optional:

        "uid":  Unique integer ID for each detector.  Computed from detector name if
            not specified.
        "pol_angle":  Quantity to specify the polarization angle.  Default assumes
            the polarization sensitive direction is aligned with the detector
            quaternion rotation.  Computed if not specified.
        "pol_leakage":  Float value "epsilon" between 0-1.  Set to zero by default.
        "pol_efficiency":  Float value "eta" = (1 - epsilon) / (1 + epsilon).  Set
            to one by default.
        "fwhm":  Quantity with the nominal beam FWHM.  Used for plotting and for
            smoothing of simulated sky signal with PySM.
        "bandcenter":  Quantity for the band center.  Used for bandpass integration
            with PySM simulations.
        "bandwidth":  Quantity for width of the band.  Used for bandpass integration
            with PySM simulations.
        "psd_net":  The detector sensitivity.  Quantity used to create a synthetic
            noise model with the DefaultNoiseModel operator.
        "psd_fknee":  Quantity used to create a synthetic noise model with the
            DefaultNoiseModel operator.
        "psd_fmin":  Quantity used to create a synthetic noise model with the
            DefaultNoiseModel operator.
        "psd_alpha":  Quantity used to create a synthetic noise model with the
            DefaultNoiseModel operator.
        "elevation_noise_a" and "elevation_noise_c":  Parameters of elevation scaling
            noise model: PSD_{out} = PSD_{ref} * (a / sin(el) + c)^2.  Only applicable
            to ground data.
        "pwv_a0", "pwv_a1" and "pwv_a2":  quadratic fit of the NET modulation by
            PWV.  Only applicable to ground data.

    Args:
        detector_data (QTable):  Table of detector properties.
        field_of_view (Quantity):  Angular diameter of the focal plane.  Used to
            increase the effective size of the focalplane when simulating atmosphere,
            etc.  Will be calculated from the detector offsets by default.
        sample_rate (Quantity):  The common (nominal) sample rate for all detectors.
        thinfp (int):  Only sample the detectors in the file.

    """

    XAXIS, YAXIS, ZAXIS = np.eye(3)

    @function_timer
    def __init__(
        self,
        detector_data=None,
        field_of_view=None,
        sample_rate=None,
        thinfp=None,
    ):
        self.detector_data = detector_data
        self.field_of_view = field_of_view
        self.sample_rate = sample_rate
        self.thinfp = thinfp
        if detector_data is not None and len(detector_data) > 0:
            # We have some dets
            self._initialize()

    @function_timer
    def _initialize(self):
        log = Logger.get()

        if self.thinfp is not None:
            # Pick only every `thinfp` pixel on the focal plane
            ndet = len(self.detector_data)
            for idet in range(ndet - 1, -1, -1):
                if int(idet // 2) % self.thinfp != 0:
                    del self.detector_data[idet]

        # Add UID if not given
        if "uid" not in self.detector_data.colnames:
            self.detector_data.add_column(
                Column(
                    name="uid", data=[name_UID(x["name"]) for x in self.detector_data]
                )
            )

        # Build index of detector to table row
        self._det_to_row = {y["name"]: x for x, y in enumerate(self.detector_data)}

        if self.field_of_view is None:
            self._compute_fov()
        self._get_pol_angles()
        self._get_pol_efficiency()
        self._get_bandpass()

    @function_timer
    def _get_bandpass(self):
        """Use the bandpass parameters to instantiate a bandpass model"""

        if "bandcenter" in self.detector_data.colnames:
            bandcenter = {}
            bandwidth = {}
            for row in self.detector_data:
                name = row["name"]
                bandcenter[name] = row["bandcenter"]
                bandwidth[name] = row["bandwidth"]
            self.bandpass = Bandpass(bandcenter, bandwidth)
        else:
            self.bandpass = None
        return

    @function_timer
    def _compute_fov(self):
        """Compute the field of view"""
        # Find the largest distance from the bore sight
        cosangs = list()
        for row in self.detector_data:
            quat = row["quat"]
            vec = qarray.rotate(quat, self.ZAXIS)
            cosangs.append(np.dot(self.ZAXIS, vec))
        mincos = np.amin(cosangs)
        # Add a very small margin to avoid numeric issues
        # in the atmospheric simulation
        self.field_of_view = 1.01 * 2.0 * np.arccos(mincos) * u.radian
        # If we just have boresight detectors, we will need to give this some non-zero
        # value.
        if self.field_of_view == 0:
            self.field_of_view = 1.0 * u.degree

    @function_timer
    def _get_pol_angles(self):
        """Get the detector polarization angles from the quaternions"""

        if "pol_angle" not in self.detector_data.colnames:
            n_rows = len(self.detector_data)
            self.detector_data.add_column(
                Column(name="pol_angle", length=n_rows, unit=u.radian)
            )
            for row in self.detector_data:
                quat = row["quat"]
                a = quat[3]
                d = quat[2]
                pol_angle = np.arctan2(2 * a * d, a**2 - d**2) % np.pi
                row["pol_angle"] = pol_angle * u.radian

    @function_timer
    def _get_pol_efficiency(self):
        """Get the polarization efficiency from polarization leakage or vice versa"""

        n_rows = len(self.detector_data)
        if ("pol_leakage" in self.detector_data.colnames) and (
            "pol_efficiency" in self.detector_data.colnames
        ):
            # Check that efficiency and leakage are consistent
            epsilon = self.detector_data["pol_leakage"]
            eta = self.detector_data["pol_efficiency"]
            np.testing.assert_allclose(
                eta,
                (1 + epsilon) / (1 - epsilon),
                rtol=1e-6,
                err_msg="inconsistent polarization leakage and efficiency",
            )
            return
        elif "pol_leakage" in self.detector_data.colnames:
            self.detector_data.add_column(
                Column(
                    name="pol_efficiency",
                    data=[(1 - x) / (1 + x) for x in self.detector_data["pol_leakage"]],
                )
            )
        elif "pol_efficiency" in self.detector_data.colnames:
            self.detector_data.add_column(
                Column(
                    name="pol_leakage",
                    data=[
                        (1 - x) / (1 + x) for x in self.detector_data["pol_efficiency"]
                    ],
                )
            )
        else:
            self.detector_data.add_column(
                Column(name="pol_efficiency", data=np.ones(n_rows))
            )
            self.detector_data.add_column(
                Column(name="pol_leakage", data=np.zeros(n_rows))
            )

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

    def __getitem__(self, key):
        return self.detector_data[self._det_to_row[key]]

    def __setitem__(self, key, value):
        if key not in self._det_to_row:
            msg = "cannot assign to non-existent detector '{}'".format(key)
            raise ValueError(msg)
        indx = self._det_to_row[key]
        if hasattr(value, "fields"):
            # numpy structured array
            if value.fields is None:
                raise ValueError("assignment value must be structured")
            for cname, ctype in value.fields.items():
                if cname not in self.detector_data.colnames:
                    msg = "assignment value element '{}' is not a det column".format(
                        cname
                    )
                    raise ValueError(msg)
                self.detector_data[indx][cname] = value[cname]
        elif hasattr(value, "colnames"):
            # table row
            for c in value.colnames:
                if c not in self.detector_data.colnames:
                    msg = "assignment value element '{}' is not a det column".format(c)
                    raise ValueError(msg)
                self.detector_data[indx][c] = value[c]
        else:
            # see if it is like a dictionary
            try:
                for k, v in value.items():
                    if k not in self.detector_data.colnames:
                        msg = (
                            "assignment value element '{}' is not a det column".format(
                                k
                            )
                        )
                        raise ValueError(msg)
                    self.detector_data[indx][k] = v
            except Exception:
                raise ValueError(
                    "assignment value must be a dictionary, Row, or structured array"
                )

    @property
    def detectors(self):
        return list(self._det_to_row.keys())

    @property
    def n_detectors(self):
        return len(self._det_to_row.keys())

    def keys(self):
        return self.detectors

    @function_timer
    def detector_groups(self, column):
        """Group detectors by a common value in one property.

        This returns a dictionary whose keys are the unique values of the specified
        detector_data column.  The values for each key are a list of detectors that
        have that value.  This can be useful for creating detector sets for data
        distribution or for considering detectors with correlations.

        Since the column values will be used for dictionary keys, the column must
        be a data type which is hashable.

        Args:
            column (str):  The detector_data column.

        Returns:
            (dict):  The detector names grouped by unique column values.

        """
        if column not in self.detector_data.colnames:
            raise RuntimeError(f"'{column}' is not a valid det data column")
        detgroups = dict()
        for d in self.detectors:
            indx = self._det_to_row[d]
            val = self.detector_data[column][indx]
            if val not in detgroups:
                detgroups[val] = list()
            detgroups[val].append(d)
        return detgroups

    def __repr__(self):
        value = "<Focalplane: {} detectors, sample_rate = {} Hz, FOV = {} deg, detectors = [".format(
            len(self.detector_data),
            self.sample_rate.to_value(u.Hz),
            self.field_of_view.to_value(u.degree),
        )
        value += "{} .. {}".format(self.detectors[0], self.detectors[-1])
        value += "]>"
        return value

    def __eq__(self, other):
        if self.sample_rate != other.sample_rate:
            return False
        if self.field_of_view != other.field_of_view:
            return False
        if self.detectors != other.detectors:
            return False
        if self.detector_data.colnames != other.detector_data.colnames:
            return False
        if not self.detector_data.values_equal(other.detector_data):
            return False
        return True

    def __ne__(self, other):
        return not self.__eq__(other)

    def load_hdf5(self, handle, comm=None, **kwargs):
        """Load the focalplane from an HDF5 group.

        Args:
            handle (h5py.Group):  The group containing the "focalplane" dataset.
            comm (MPI.Comm):  If loading from a file, optional communicator to broadcast
                across.

        Returns:
            None

        """
        log = Logger.get()

        # Determine if we need to broadcast results.  This occurs if only one process
        # has the file open but the communicator has more than one process.
        need_bcast = hdf5_use_serial(handle, comm)

        if self.detector_data is not None:
            raise RuntimeError("Reading detector data over existing table")

        if handle is not None:
            self.detector_data = read_table_hdf5(handle, path="focalplane")

        if need_bcast and comm is not None:
            self.detector_data = comm.bcast(self.detector_data, root=0)

        # Only use the sampling rate recorded in the file if it was not
        # overridden in the constructor
        if self.sample_rate is None:
            self.sample_rate = self.detector_data.meta["sample_rate"]
        if self.field_of_view is None and "field_of_view" in self.detector_data.meta:
            self.field_of_view = self.detector_data.meta["field_of_view"]

        # Initialize other properties
        self._initialize()

        log.debug_rank(
            f"Focalplane has {len(self.detector_data)} detectors that span "
            f"{self.field_of_view.to_value(u.deg):.3f} degrees and are sampled at "
            f"{self.sample_rate.to_value(u.Hz)} Hz.",
            comm=comm,
        )

    def save_hdf5(self, handle, comm=None, **kwargs):
        """Save the focalplane to an HDF5 group.

        Args:
            handle (h5py.Group):  The parent group of the focalplane dataset.
            comm (MPI.Comm):  If loading from a file, optional communicator to broadcast
                across.

        Returns:
            None

        """
        self.detector_data.meta["sample_rate"] = self.sample_rate
        self.detector_data.meta["field_of_view"] = self.field_of_view
        table_write_parallel_hdf5(self.detector_data, handle, "focalplane", comm=comm)

detector_data = detector_data instance-attribute

detectors property

field_of_view = field_of_view instance-attribute

n_detectors property

sample_rate = sample_rate instance-attribute

thinfp = thinfp instance-attribute

__contains__(key)

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

__eq__(other)

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def __eq__(self, other):
    if self.sample_rate != other.sample_rate:
        return False
    if self.field_of_view != other.field_of_view:
        return False
    if self.detectors != other.detectors:
        return False
    if self.detector_data.colnames != other.detector_data.colnames:
        return False
    if not self.detector_data.values_equal(other.detector_data):
        return False
    return True

__getitem__(key)

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def __getitem__(self, key):
    return self.detector_data[self._det_to_row[key]]

__init__(detector_data=None, field_of_view=None, sample_rate=None, thinfp=None)

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@function_timer
def __init__(
    self,
    detector_data=None,
    field_of_view=None,
    sample_rate=None,
    thinfp=None,
):
    self.detector_data = detector_data
    self.field_of_view = field_of_view
    self.sample_rate = sample_rate
    self.thinfp = thinfp
    if detector_data is not None and len(detector_data) > 0:
        # We have some dets
        self._initialize()

__ne__(other)

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def __ne__(self, other):
    return not self.__eq__(other)

__repr__()

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def __repr__(self):
    value = "<Focalplane: {} detectors, sample_rate = {} Hz, FOV = {} deg, detectors = [".format(
        len(self.detector_data),
        self.sample_rate.to_value(u.Hz),
        self.field_of_view.to_value(u.degree),
    )
    value += "{} .. {}".format(self.detectors[0], self.detectors[-1])
    value += "]>"
    return value

__setitem__(key, value)

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def __setitem__(self, key, value):
    if key not in self._det_to_row:
        msg = "cannot assign to non-existent detector '{}'".format(key)
        raise ValueError(msg)
    indx = self._det_to_row[key]
    if hasattr(value, "fields"):
        # numpy structured array
        if value.fields is None:
            raise ValueError("assignment value must be structured")
        for cname, ctype in value.fields.items():
            if cname not in self.detector_data.colnames:
                msg = "assignment value element '{}' is not a det column".format(
                    cname
                )
                raise ValueError(msg)
            self.detector_data[indx][cname] = value[cname]
    elif hasattr(value, "colnames"):
        # table row
        for c in value.colnames:
            if c not in self.detector_data.colnames:
                msg = "assignment value element '{}' is not a det column".format(c)
                raise ValueError(msg)
            self.detector_data[indx][c] = value[c]
    else:
        # see if it is like a dictionary
        try:
            for k, v in value.items():
                if k not in self.detector_data.colnames:
                    msg = (
                        "assignment value element '{}' is not a det column".format(
                            k
                        )
                    )
                    raise ValueError(msg)
                self.detector_data[indx][k] = v
        except Exception:
            raise ValueError(
                "assignment value must be a dictionary, Row, or structured array"
            )

_compute_fov()

Compute the field of view

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@function_timer
def _compute_fov(self):
    """Compute the field of view"""
    # Find the largest distance from the bore sight
    cosangs = list()
    for row in self.detector_data:
        quat = row["quat"]
        vec = qarray.rotate(quat, self.ZAXIS)
        cosangs.append(np.dot(self.ZAXIS, vec))
    mincos = np.amin(cosangs)
    # Add a very small margin to avoid numeric issues
    # in the atmospheric simulation
    self.field_of_view = 1.01 * 2.0 * np.arccos(mincos) * u.radian
    # If we just have boresight detectors, we will need to give this some non-zero
    # value.
    if self.field_of_view == 0:
        self.field_of_view = 1.0 * u.degree

_get_bandpass()

Use the bandpass parameters to instantiate a bandpass model

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@function_timer
def _get_bandpass(self):
    """Use the bandpass parameters to instantiate a bandpass model"""

    if "bandcenter" in self.detector_data.colnames:
        bandcenter = {}
        bandwidth = {}
        for row in self.detector_data:
            name = row["name"]
            bandcenter[name] = row["bandcenter"]
            bandwidth[name] = row["bandwidth"]
        self.bandpass = Bandpass(bandcenter, bandwidth)
    else:
        self.bandpass = None
    return

_get_pol_angles()

Get the detector polarization angles from the quaternions

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@function_timer
def _get_pol_angles(self):
    """Get the detector polarization angles from the quaternions"""

    if "pol_angle" not in self.detector_data.colnames:
        n_rows = len(self.detector_data)
        self.detector_data.add_column(
            Column(name="pol_angle", length=n_rows, unit=u.radian)
        )
        for row in self.detector_data:
            quat = row["quat"]
            a = quat[3]
            d = quat[2]
            pol_angle = np.arctan2(2 * a * d, a**2 - d**2) % np.pi
            row["pol_angle"] = pol_angle * u.radian

_get_pol_efficiency()

Get the polarization efficiency from polarization leakage or vice versa

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@function_timer
def _get_pol_efficiency(self):
    """Get the polarization efficiency from polarization leakage or vice versa"""

    n_rows = len(self.detector_data)
    if ("pol_leakage" in self.detector_data.colnames) and (
        "pol_efficiency" in self.detector_data.colnames
    ):
        # Check that efficiency and leakage are consistent
        epsilon = self.detector_data["pol_leakage"]
        eta = self.detector_data["pol_efficiency"]
        np.testing.assert_allclose(
            eta,
            (1 + epsilon) / (1 - epsilon),
            rtol=1e-6,
            err_msg="inconsistent polarization leakage and efficiency",
        )
        return
    elif "pol_leakage" in self.detector_data.colnames:
        self.detector_data.add_column(
            Column(
                name="pol_efficiency",
                data=[(1 - x) / (1 + x) for x in self.detector_data["pol_leakage"]],
            )
        )
    elif "pol_efficiency" in self.detector_data.colnames:
        self.detector_data.add_column(
            Column(
                name="pol_leakage",
                data=[
                    (1 - x) / (1 + x) for x in self.detector_data["pol_efficiency"]
                ],
            )
        )
    else:
        self.detector_data.add_column(
            Column(name="pol_efficiency", data=np.ones(n_rows))
        )
        self.detector_data.add_column(
            Column(name="pol_leakage", data=np.zeros(n_rows))
        )

_initialize()

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@function_timer
def _initialize(self):
    log = Logger.get()

    if self.thinfp is not None:
        # Pick only every `thinfp` pixel on the focal plane
        ndet = len(self.detector_data)
        for idet in range(ndet - 1, -1, -1):
            if int(idet // 2) % self.thinfp != 0:
                del self.detector_data[idet]

    # Add UID if not given
    if "uid" not in self.detector_data.colnames:
        self.detector_data.add_column(
            Column(
                name="uid", data=[name_UID(x["name"]) for x in self.detector_data]
            )
        )

    # Build index of detector to table row
    self._det_to_row = {y["name"]: x for x, y in enumerate(self.detector_data)}

    if self.field_of_view is None:
        self._compute_fov()
    self._get_pol_angles()
    self._get_pol_efficiency()
    self._get_bandpass()

detector_groups(column)

Group detectors by a common value in one property.

This returns a dictionary whose keys are the unique values of the specified detector_data column. The values for each key are a list of detectors that have that value. This can be useful for creating detector sets for data distribution or for considering detectors with correlations.

Since the column values will be used for dictionary keys, the column must be a data type which is hashable.

Parameters:

Name Type Description Default
column str

The detector_data column.

 required

Returns:

Type Description
dict

The detector names grouped by unique column values.
Source code in toast/instrument.py 
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@function_timer
def detector_groups(self, column):
    """Group detectors by a common value in one property.

    This returns a dictionary whose keys are the unique values of the specified
    detector_data column.  The values for each key are a list of detectors that
    have that value.  This can be useful for creating detector sets for data
    distribution or for considering detectors with correlations.

    Since the column values will be used for dictionary keys, the column must
    be a data type which is hashable.

    Args:
        column (str):  The detector_data column.

    Returns:
        (dict):  The detector names grouped by unique column values.

    """
    if column not in self.detector_data.colnames:
        raise RuntimeError(f"'{column}' is not a valid det data column")
    detgroups = dict()
    for d in self.detectors:
        indx = self._det_to_row[d]
        val = self.detector_data[column][indx]
        if val not in detgroups:
            detgroups[val] = list()
        detgroups[val].append(d)
    return detgroups

keys()

Source code in toast/instrument.py 
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def keys(self):
    return self.detectors

load_hdf5(handle, comm=None, **kwargs)

Load the focalplane from an HDF5 group.

Parameters:

Name Type Description Default
handle Group

The group containing the "focalplane" dataset.

 required
comm Comm

If loading from a file, optional communicator to broadcast across.

 None

Returns:

Type Description

None
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def load_hdf5(self, handle, comm=None, **kwargs):
    """Load the focalplane from an HDF5 group.

    Args:
        handle (h5py.Group):  The group containing the "focalplane" dataset.
        comm (MPI.Comm):  If loading from a file, optional communicator to broadcast
            across.

    Returns:
        None

    """
    log = Logger.get()

    # Determine if we need to broadcast results.  This occurs if only one process
    # has the file open but the communicator has more than one process.
    need_bcast = hdf5_use_serial(handle, comm)

    if self.detector_data is not None:
        raise RuntimeError("Reading detector data over existing table")

    if handle is not None:
        self.detector_data = read_table_hdf5(handle, path="focalplane")

    if need_bcast and comm is not None:
        self.detector_data = comm.bcast(self.detector_data, root=0)

    # Only use the sampling rate recorded in the file if it was not
    # overridden in the constructor
    if self.sample_rate is None:
        self.sample_rate = self.detector_data.meta["sample_rate"]
    if self.field_of_view is None and "field_of_view" in self.detector_data.meta:
        self.field_of_view = self.detector_data.meta["field_of_view"]

    # Initialize other properties
    self._initialize()

    log.debug_rank(
        f"Focalplane has {len(self.detector_data)} detectors that span "
        f"{self.field_of_view.to_value(u.deg):.3f} degrees and are sampled at "
        f"{self.sample_rate.to_value(u.Hz)} Hz.",
        comm=comm,
    )

save_hdf5(handle, comm=None, **kwargs)

Save the focalplane to an HDF5 group.

Parameters:

Name Type Description Default
handle Group

The parent group of the focalplane dataset.

 required
comm Comm

If loading from a file, optional communicator to broadcast across.

 None

Returns:

Type Description

None
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def save_hdf5(self, handle, comm=None, **kwargs):
    """Save the focalplane to an HDF5 group.

    Args:
        handle (h5py.Group):  The parent group of the focalplane dataset.
        comm (MPI.Comm):  If loading from a file, optional communicator to broadcast
            across.

    Returns:
        None

    """
    self.detector_data.meta["sample_rate"] = self.sample_rate
    self.detector_data.meta["field_of_view"] = self.field_of_view
    table_write_parallel_hdf5(self.detector_data, handle, "focalplane", comm=comm)

toast.instrument.Telescope

Bases: object

Class representing telescope properties for one observation.

Parameters:

Name Type Description Default
name str

The name of the telescope.

 required
uid int

The Unique ID of the telescope. If not specified, constructed from a hash of the site name.

 None
focalplane Focalplane

The focalplane for this observation.

 None
site Site

The site of the telescope for this observation.

 None
Source code in toast/instrument.py 
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class Telescope(object):
    """Class representing telescope properties for one observation.

    Args:
        name (str):  The name of the telescope.
        uid (int):  The Unique ID of the telescope.  If not specified, constructed from
            a hash of the site name.
        focalplane (Focalplane):  The focalplane for this observation.
        site (Site):  The site of the telescope for this observation.

    """

    def __init__(self, name, uid=None, focalplane=None, site=None):
        self.name = name
        self.uid = uid
        if self.uid is None:
            self.uid = name_UID(name)
        if not isinstance(focalplane, Focalplane):
            raise RuntimeError("focalplane should be a Focalplane class instance")
        self.focalplane = focalplane
        if not isinstance(site, Site):
            raise RuntimeError("site should be a Site class instance")
        self.site = site

    def __repr__(self):
        value = "<Telescope '{}': uid = {}, site = {}, ".format(
            self.name,
            self.uid,
            self.site,
        )
        value += "focalplane = {}".format(self.focalplane.__repr__())
        value += ">"
        return value

    def __eq__(self, other):
        if self.name != other.name:
            return False
        if self.uid != other.uid:
            return False
        if self.site != other.site:
            return False
        if self.focalplane != other.focalplane:
            return False
        return True

    def __ne__(self, other):
        return not self.__eq__(other)

focalplane = focalplane instance-attribute

name = name instance-attribute

site = site instance-attribute

uid = uid instance-attribute

__eq__(other)

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def __eq__(self, other):
    if self.name != other.name:
        return False
    if self.uid != other.uid:
        return False
    if self.site != other.site:
        return False
    if self.focalplane != other.focalplane:
        return False
    return True

__init__(name, uid=None, focalplane=None, site=None)

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def __init__(self, name, uid=None, focalplane=None, site=None):
    self.name = name
    self.uid = uid
    if self.uid is None:
        self.uid = name_UID(name)
    if not isinstance(focalplane, Focalplane):
        raise RuntimeError("focalplane should be a Focalplane class instance")
    self.focalplane = focalplane
    if not isinstance(site, Site):
        raise RuntimeError("site should be a Site class instance")
    self.site = site

__ne__(other)

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def __ne__(self, other):
    return not self.__eq__(other)

__repr__()

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def __repr__(self):
    value = "<Telescope '{}': uid = {}, site = {}, ".format(
        self.name,
        self.uid,
        self.site,
    )
    value += "focalplane = {}".format(self.focalplane.__repr__())
    value += ">"
    return value

toast.instrument.Session

Bases: object

Class representing an observing session.

A session consists of multiple Observation instances with different sets of detectors and possibly different sample rates / times. However these observations are on the same physical telescope and over the same broad time range. A session simply tracks that time range and a unique ID which can be used to group the relevant observations.

Parameters:

Name Type Description Default
name str

The name of the session.

 required
uid int

The Unique ID of the session. If not specified, it will be constructed from a hash of the name and the optional start/stop times.

 None
start datetime

The overall start of the session.

 None
end datetime

The overall end of the session.

 None
Source code in toast/instrument.py 
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class Session(object):
    """Class representing an observing session.

    A session consists of multiple Observation instances with different sets of
    detectors and possibly different sample rates / times.  However these
    observations are on the same physical telescope and over the same broad
    time range.  A session simply tracks that time range and a unique ID which
    can be used to group the relevant observations.

    Args:
        name (str):  The name of the session.
        uid (int):  The Unique ID of the session.  If not specified, it will be
            constructed from a hash of the name and the optional start/stop times.
        start (datetime):  The overall start of the session.
        end (datetime):  The overall end of the session.

    """

    def __init__(self, name, uid=None, start=None, end=None):
        self.name = name
        for t in start, end:
            if t is not None and not isinstance(t, datetime.datetime):
                raise RuntimeError("Session start/end must be a datetime or None")
        if uid is not None:
            self.uid = uid
        else:
            # Append start and end times to the session name before
            # evaluating the hash.  This reduces the risk of clashing UIDs.
            session_name = name
            if start is not None:
                session_name += start.ctime()
            if end is not None:
                session_name += end.ctime()
            self.uid = name_UID(session_name)
        self.start = start
        self.end = end

    def __repr__(self):
        value = "<Session '{}': uid = {}, start = {}, end = {}".format(
            self.name, self.uid, self.start, self.end
        )
        value += ">"
        return value

    def __eq__(self, other):
        if self.name != other.name:
            return False
        if self.uid != other.uid:
            return False
        if self.start != other.start:
            return False
        if self.end != other.end:
            return False
        return True

    def __ne__(self, other):
        return not self.__eq__(other)

end = end instance-attribute

name = name instance-attribute

start = start instance-attribute

uid = uid instance-attribute

__eq__(other)

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def __eq__(self, other):
    if self.name != other.name:
        return False
    if self.uid != other.uid:
        return False
    if self.start != other.start:
        return False
    if self.end != other.end:
        return False
    return True

__init__(name, uid=None, start=None, end=None)

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def __init__(self, name, uid=None, start=None, end=None):
    self.name = name
    for t in start, end:
        if t is not None and not isinstance(t, datetime.datetime):
            raise RuntimeError("Session start/end must be a datetime or None")
    if uid is not None:
        self.uid = uid
    else:
        # Append start and end times to the session name before
        # evaluating the hash.  This reduces the risk of clashing UIDs.
        session_name = name
        if start is not None:
            session_name += start.ctime()
        if end is not None:
            session_name += end.ctime()
        self.uid = name_UID(session_name)
    self.start = start
    self.end = end

__ne__(other)

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def __ne__(self, other):
    return not self.__eq__(other)

__repr__()

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def __repr__(self):
    value = "<Session '{}': uid = {}, start = {}, end = {}".format(
        self.name, self.uid, self.start, self.end
    )
    value += ">"
    return value