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get_infos_obs

Retrieve instrument, mask, and spectral information.

get_ifu_table(i_wl, filtname='YH', instrument='SPHERE-IFS', verbose=False, display=False)

Return spectral information for an instrumental IFU setup.

Parameters:

Name Type Description Default
i_wl int or list of int

One channel, a two-index channel range, or channels to display.

 required
filtname str

IFU filter name.

 "YH"
instrument str

Instrument name.

 "SPHERE-IFS"
verbose bool

Whether to print spectral coverage.

 False
display bool

Whether to display the selected channels.

 False

Returns:

Type Description
float or ndarray

Selected wavelength or wavelengths in micrometres.
Source code in src/amical/get_infos_obs.py 
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def get_ifu_table(
    i_wl, filtname="YH", instrument="SPHERE-IFS", verbose=False, display=False
):
    """Return spectral information for an instrumental IFU setup.

    Parameters
    ----------
    i_wl : int or list of int
        One channel, a two-index channel range, or channels to display.
    filtname : str, default="YH"
        IFU filter name.
    instrument : str, default="SPHERE-IFS"
        Instrument name.
    verbose : bool, default=False
        Whether to print spectral coverage.
    display : bool, default=False
        Whether to display the selected channels.

    Returns
    -------
    float or numpy.ndarray
        Selected wavelength or wavelengths in micrometres.
    """
    wl = get_wavelength(instrument, filtname) * 1e6

    if verbose:
        print(f"\nInstrument: {instrument}, spectral range: {filtname}")
        print("-----------------------------")
        print(
            f"spectral coverage: {wl[0]:2.2f} - {wl[-1]:2.2f} µm (step = {np.diff(wl)[0]:2.2f})"
        )

    one_wl = True
    multiple_wl = False
    if isinstance(i_wl, list) and (len(i_wl) == 2):
        one_wl = False
        wl_range = wl[i_wl[0] : i_wl[1]]
        sp_range = np.arange(i_wl[0], i_wl[1], 1)
    elif isinstance(i_wl, list) and (len(i_wl) > 2):
        multiple_wl = True
        one_wl = False
    elif i_wl is None:
        one_wl = False
        sp_range = np.arange(len(wl))
        wl_range = wl

    if display:
        from matplotlib import pyplot as plt

        plt.figure(figsize=(4, 3))
        plt.title("--- SPECTRAL INFORMATION (IFU)---")
        plt.plot(wl, label="All spectral channels")
        if one_wl:
            plt.plot(
                np.arange(len(wl))[i_wl],
                wl[i_wl],
                "ro",
                label=f"Selected ({wl[np.atleast_1d(i_wl)[0]]:2.2f} µm)",
            )
        elif multiple_wl:
            plt.plot(
                i_wl,
                wl[i_wl],
                "ro",
                label="Selected",
            )
        else:
            plt.plot(
                sp_range,
                wl_range,
                lw=5,
                alpha=0.5,
                label=f"Selected ({wl_range[0]:2.2f}-{wl_range[-1]:2.2f} µm)",
            )
        plt.legend()
        plt.xlabel("Spectral channel")
        plt.ylabel("Wavelength [µm]")
        plt.tight_layout()

    if one_wl:
        output = wl[i_wl]
    elif multiple_wl:
        output = np.array(wl[i_wl])
    else:
        output = wl_range
    return output

get_mask(ins, mask, first=0)

Return the saved coordinates for an instrument mask.

Parameters:

Name Type Description Default
ins str

Instrument name.

 required
mask str

Mask name.

 required
first int

Index placed first in the returned coordinate sequence.

 0

Returns:

Type Description
ndarray or None

Selected mask-hole coordinates, or None for an unknown mask.
Source code in src/amical/get_infos_obs.py 
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def get_mask(ins, mask, first=0):
    """Return the saved coordinates for an instrument mask.

    Parameters
    ----------
    ins : str
        Instrument name.
    mask : str
        Mask name.
    first : int, default=0
        Index placed first in the returned coordinate sequence.

    Returns
    -------
    numpy.ndarray or None
        Selected mask-hole coordinates, or None for an unknown mask.
    """

    pupil_visir = 8.0
    pupil_visir_mm = 17.67
    off = 0.3
    dic_mask = {
        "ERIS": {
            "g7_K": np.array(
                [
                    [-1.432, -2.901],
                    [2.849, -1.542],
                    [-3.037, 0.084],
                    [-0.464, 1.476],
                    [-1.278, 2.974],
                    [0.417, 2.932],
                    [2.929, 1.402],
                ]
            ),
            "g9_K": np.array(
                [
                    [-1.291, -2.504],
                    [-0.057, -2.554],
                    [-3.079, -1.353],
                    [2.5, -0.535],
                    [3.164, 0.493],
                    [2.036, 2.691],
                    [-1.085, 1.756],
                    [-2.977, 0.778],
                    [-1.655, 2.852],
                ]
            ),
            "g23_K": np.array(
                [
                    [-0.844, -3.306],
                    [0.793, -3.316],
                    [1.343, -3.32],
                    [-2.207, -2.83],
                    [-0.836, -2.353],
                    [2.162, -2.852],
                    [2.712, -1.91],
                    [-3.006, -1.396],
                    [0.257, -1.414],
                    [2.99, -1.437],
                    [-3.548, -0.447],
                    [-1.355, 0.477],
                    [-3.263, 0.022],
                    [-2.988, 1.434],
                    [3.554, 0.451],
                    [3.283, 0.923],
                    [2.738, 1.873],
                    [-1.346, 2.368],
                    [-2.165, 2.845],
                    [1.653, 2.826],
                    [-0.252, 3.31],
                    [0.287, 3.307],
                    [1.386, 3.303],
                ]
            ),
            "g7_L": np.array(
                [
                    [-1.441, -2.93],
                    [2.875, -1.558],
                    [-3.07, 0.078],
                    [-0.466, 1.487],
                    [-1.289, 3.003],
                    [0.421, 2.957],
                    [2.955, 1.403],
                ]
            ),
            "g9_L": np.array(
                [
                    [-1.296, -2.533],
                    [-0.056, -2.583],
                    [-3.113, -1.372],
                    [2.529, -0.549],
                    [3.202, 0.506],
                    [2.058, 2.716],
                    [-1.093, 1.773],
                    [-3.012, 0.788],
                    [-1.672, 2.885],
                ]
            ),
            "g23_L": np.array(
                [
                    [-0.849, -3.336],
                    [0.804, -3.349],
                    [1.358, -3.353],
                    [-2.224, -2.854],
                    [-0.841, -2.375],
                    [2.18, -2.879],
                    [2.737, -1.929],
                    [-3.039, -1.413],
                    [0.265, -1.428],
                    [3.019, -1.446],
                    [-3.588, -0.455],
                    [-1.375, 0.484],
                    [-3.307, 0.024],
                    [-3.025, 1.449],
                    [3.59, 0.458],
                    [3.311, 0.933],
                    [2.763, 1.891],
                    [-1.355, 2.392],
                    [-2.184, 2.879],
                    [1.672, 2.853],
                    [-0.251, 3.339],
                    [0.292, 3.335],
                    [1.401, 3.335],
                ]
            ),
        },
        "NIRISS": {
            "g7": np.array(
                [
                    [0, -2.64],
                    [-2.28631, 0],
                    [2.28631, -1.32],
                    [-2.28631, 1.32],
                    [-1.14315, 1.98],
                    [2.28631, 1.32],
                    [1.14315, 1.98],
                ]
            ),
            "g7_bis": np.array(
                [
                    [0, 2.9920001],
                    [2.2672534, 0.37400016],
                    [-2.2672534, 1.6829998],
                    [2.2672534, -0.93499988],
                    [1.1336316, -1.5895000],
                    [-2.2672534, -0.93500012],
                    [-1.1336313, -1.5895000],
                ]
            ),
            "g7_sb": np.array(
                [
                    [0, -2.64],  # 0
                    [-2.28631, 0],  # 1
                    [-2.28631 + off, 0],
                    [-2.28631 - off / np.sqrt(2), 0 + off / np.sqrt(2)],
                    [-2.28631 - off / np.sqrt(2), 0 - off / np.sqrt(2)],
                    [2.28631, -1.32],  # 2
                    [-2.28631, 1.32],  # 3
                    [-1.14315, 1.98],  # 4
                    [-1.14315 + off, 1.98],
                    [-1.14315 - off / np.sqrt(2), 1.98 + off / np.sqrt(2)],
                    [-1.14315 - off / np.sqrt(2), 1.98 - off / np.sqrt(2)],
                    [2.28631, 1.32],  # 5
                    [2.28631 + off, 1.32],
                    [2.28631 - off / np.sqrt(2), 1.32 + off / np.sqrt(2)],
                    [2.28631 - off / np.sqrt(2), 1.32 - off / np.sqrt(2)],
                    [1.14315, 1.98],  # 6
                ]
            ),
        },
        "GLINT": {
            "g4": np.array(
                [[2.725, 2.317], [-2.812, 1.685], [-2.469, -1.496], [-0.502, -2.363]]
            )
        },
        "NACO": {
            "g7": np.array(
                [
                    [-3.51064, -1.99373],
                    [-3.51064, 2.49014],
                    [-1.56907, 1.36918],
                    [-1.56907, 3.61111],
                    [0.372507, -4.23566],
                    [2.31408, 3.61111],
                    [4.25565, 0.248215],
                ]
            )
            * (8 / 10.0),
        },
        "SPHERE": {
            "g7": 1.05
            * np.array(
                [
                    [-1.46, 2.87],
                    [1.46, 2.87],
                    [-2.92, 0.34],
                    [-1.46, -0.51],
                    [-2.92, -1.35],
                    [2.92, -1.35],
                    [0, -3.04],
                ]
            )
        },
        "SPHERE-IFS": {
            "g7": 1
            * np.array(
                [
                    [-2.07, 2.71],
                    [0.98, 3.27],
                    [-3.11, -0.2],
                    [-1.43, -0.81],
                    [-2.79, -1.96],
                    [3.3, -0.85],
                    [0.58, -3.17],
                ]
            )
        },
        "VISIR": {
            "g7": (pupil_visir / pupil_visir_mm)
            * np.array(
                [
                    [-5.707, -2.885],
                    [-5.834, 3.804],
                    [0.099, 7.271],
                    [7.989, 0.422],
                    [3.989, -6.481],
                    [-3.790, -6.481],
                    [-1.928, -2.974],
                ]
            ),
        },
        "VAMPIRES": {
            "g18": np.array(
                [
                    [0.821457, 2.34684],
                    [-2.34960, 1.49034],
                    [-2.54456, 2.55259],
                    [1.64392, 3.04681],
                    [2.73751, -0.321102],
                    [1.38503, -3.31443],
                    [-3.19337, -1.68413],
                    [3.05126, 0.560011],
                    [-2.76083, 1.14035],
                    [3.02995, -1.91449],
                    [0.117786, 3.59025],
                    [-0.802156, 3.42140],
                    [-1.47228, -3.28982],
                    [-1.95968, -0.634178],
                    # [-3.29085, -1.15300],
                    [0.876319, -3.13328],
                    [2.01253, -1.55220],
                    [-2.07847, -2.57755],
                ]
            )
        },
    }

    #

    try:
        xycoords = dic_mask[ins][mask]
        nrand = [first]
        for x in np.arange(len(xycoords)):
            if x not in nrand:
                nrand.append(x)
        xycoords_sel = xycoords[nrand]
    except KeyError:
        rprint(
            f"[red]\n-- Error: maskname ({mask}) unknown for {ins}.", file=sys.stderr
        )
        xycoords_sel = None
    return xycoords_sel

get_wavelength(ins, filtname)

Return the central wavelength and width for an instrument filter.

Parameters:

Name Type Description Default
ins str

Instrument name.

 required
filtname str

Filter name.

 required

Returns:

Type Description
ndarray

Filter information in metres.

Raises:

Type Description
KeyError

If the instrument or filter is unknown.
Source code in src/amical/get_infos_obs.py 
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def get_wavelength(ins, filtname):
    """Return the central wavelength and width for an instrument filter.

    Parameters
    ----------
    ins : str
        Instrument name.
    filtname : str
        Filter name.

    Returns
    -------
    numpy.ndarray
        Filter information in metres.

    Raises
    ------
    KeyError
        If the instrument or filter is unknown.
    """
    from astropy.io import fits

    datadir = importlib_resources.files("amical") / "internal_data"
    YJfile = datadir / "ifs_wave_YJ.fits"
    YJHfile = datadir / "ifs_wave_YJH.fits"

    with fits.open(YJfile) as fd:
        wave_YJ = fd[0].data
    with fits.open(YJHfile) as fd:
        wave_YJH = fd[0].data

    dic_filt = {
        "ERIS": {
            "J": [1.28, 0.20],
            "H": [1.66, 0.31],
            "Ks": [2.18, 0.39],
            "Short-Lp": [3.32, 0.43],
            "Lp": [3.79, 0.60],
            "L-Broad": [3.57, 1.04],
            "Mp": [4.78, 0.58],
            "Pa-b": [1.282, 0.021],
            "Fe-II": [1.644, 0.020],
            "H2-cont": [2.068, 0.064],
            "H2-1-0S": [2.120, 0.020],
            "Br-g": [2.172, 0.020],
            "K-peak": [2.198, 0.099],
            "IB-2.42": [2.420, 0.049],
            "IB-2.48": [2.479, 0.051],
            "Br-a-cont": [3.965, 0.108],
            "Br-a": [4.051, 0.023],
        },
        "NIRISS": {
            "F277W": [2.776, 0.715],
            "F380M": [3.828, 0.205],
            "F430M": [4.286, 0.202],
            "F480M": [4.817, 0.298],
        },
        "SPHERE": {
            "H2": [1.593, 0.052],
            "H3": [1.667, 0.054],
            "H4": [1.733, 0.057],
            "K1": [2.110, 0.102],
            "K2": [2.251, 0.109],
            "CntH": [1.573, 0.023],
            "CntK1": [2.091, 0.034],
            "CntK2": [2.266, 0.032],
        },
        "SPHERE-IFS": {"YJ": wave_YJ, "YH": wave_YJH},
        "GLINT": {"F155": [1.55, 0.01], "F430": [4.3, 0.01]},
        "VISIR": {"10_5_SAM": [10.56, 0.37], "11_3_SAM": [11.23, 0.55]},
        "VAMPIRES": {"750-50": [0.75, 0.05]},
    }

    if ins not in dic_filt.keys():
        raise KeyError(
            f"--- Error: instrument <{ins}> not found ---\n"
            "Available: %s" % list(dic_filt.keys())
        )
    if filtname not in dic_filt[ins]:
        raise KeyError(
            f"Missing input: filtname <{filtname}> not found for {ins} (Available: {list(dic_filt[ins])})"
        )
    return np.array(dic_filt[ins][filtname]) * 1e-6