NIRISS Calibration with AMICAL¶

In this tutorial, we will read in simulated NIRISS AMI data using AMICAL, for a target-calibrator pair, and then visualize the data and extract calibrated observables.

In [ ]:

import os
from astropy.io import fits
from matplotlib import pyplot as plt
import amical

In [ ]:

plt.close("all")

In [ ]:

datadir = "NRM_DATA/"

This example comes with 2 NIRISS simulated dataset representing a relatively faint binary star (dm = 6) @ 4.3 μm :

• λ/D: sep = 147.7, theta = 46.6
• λ/D: sep = 302.0, theta = 260.9

In [ ]:

sep = 147.7  # binary separation [mas]
theta = 46.6  # position angle (pa) [deg]
dm = 6.0  # contrast ratio [mag]

In our simulated data, _t is for target (astronomical scene) and _c for calibrator (point source)/

We point the path to target and calibrator files, then open these fits files as cubes.

Reading in data¶

In [ ]:

file_t = os.path.join(
    datadir,
    "t_binary_s={:2.1f}mas_mag=6.0_dm={:2.1f}_posang={:2.1f}__F430M_81_flat_x11__00.fits".format(
        sep, dm, theta
    ),
)
file_c = os.path.join(
    datadir,
    "c_binary_s={:2.1f}mas_mag=6.0_dm={:2.1f}_posang={:2.1f}__F430M_81_flat_x11__00.fits".format(
        sep, dm, theta
    ),
)

In [ ]:

hdu = fits.open(file_t)
cube_t = hdu[0].data
hdu.close()

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hdu = fits.open(file_c)
cube_c = hdu[0].data
hdu.close()

Additional cleaning steps are required for real NIRISS data or MIRAGE data (bad pixel, centering, and background).

See example_SPHERE.py for more details.

In [ ]:

#  AMI parameters (refer to the docstrings of `extract_bs` for details)
params_ami = {
    "peakmethod": "fft",
    "bs_multi_tri": False,
    "maskname": "g7",
    "fw_splodge": 0.7,
}

Extracting Observables¶

Extract raw complex observables for the target and the calibrator: Uses the core of the pipeline (amical/mf_pipeline/bispect.py)

This by default produces

• summary plots of the mask used,
• the UV plane amplitude,
• • together with real and imaginary parts, amplitude and phase;
• splodges: the mask sampling points should line up with the Fourier power;
• ensembles showing the variation in closure phase and visibility; and
• covariance matrices of visibilities and closure phases.

In [ ]:

bs_t = amical.extract_bs(
    cube_t, file_t, targetname="fakebinary", **params_ami, display=True
)
bs_c = amical.extract_bs(
    cube_c, file_c, targetname="fakepsf", **params_ami, display=False
)

Calibration¶

Now we calibrate the raw data to get calibrated V2 and CP.

bs_c can be a single calibrator result or a list of calibrators.

See amical/calibration.py for details.

In [ ]:

cal = amical.calibrate(bs_t, bs_c)

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# Display and save the results as oifits
amical.show(cal)
dic = amical.save(cal, oifits_file="example_fakebinary_NIRISS.oifits", fake_obj=True)