# Getting Started¶

## The basics¶

The core of AnisoCADO is centred around the AnalyticalScaoPsf object. When created it runs a series of setup routines, including creating a PSF kernel based on the parameters it is passed during the initialisation. This original PSF kernel is valid for the on-sky coordinate of the natural guide star (NGS) used to drive the AO correction. It is the “optimal” PSF, and can always be called up using .psf_on_axis

from anisocado import AnalyticalScaoPsf

psf = AnalyticalScaoPsf(N=512, wavelength=2.15)  # um
kernel = psf.psf_on_axis


The Strehl ratio of this kernel can be found by calling:

psf.strehl_ratio


The quality of the AO correction reduces as one moves away from the guide star. To sample the PSF at a specific distance from the guide star (i.e. off axis), use the method shift_off_axis(). AnisoCADO calculates a new PSF kernel for this coordinate, which is both returned by the function, and stored internally in .psf_latest

kernel = psf.shift_off_axis(15, -5)   # arcsec
# saved for later under:
kernel = psf.psf_latest


The returned kernel object is a numpy array. However AnisoCADO also produces a FITS HDU object from the psf_latest array when .hdu is called:

image_hdu = psf.hdu


image_hdu is an ImageHDU object, and can therefore be saved to disk using the standard astropy method .writeto:

image_hdu.writeto("new_scao_psf.fits")


## Example: Off-axis Stehl ratios¶

As an example of using the AnisoCADO API, let’s determine the Strehl ratio for different wavelengths as we move away from the (on-axis) guide star for the atmospheric profile EsoMedian

sr_list = {}
wave_list = [0.87, 1.05, 1.25, 1.65, 2.15]
for wave in wave_list:
sr_list[wave] = []

# Generate a PSF object for wavelength wave in um
psf = AnalyticalScaoPsf(N=512, wavelength=wave, profile_name="EsoMedian")

# Shift the PSF to different distances from the NGS
off_axis_positions = range(0, 41, 2)
for x in off_axis_positions:
psf.shift_off_axis(x, 0)
sr_list[wave] += [psf.strehl_ratio]


To plot the results

for wave in wave_list:
plt.plot(off_axis_positions, sr_list[wave], label="{} um".format(wave))

plt.legend()
plt.xlabel("Distance from NGS [arcsec]")
plt.ylabel("Strehl Ratio")
plt.xlim(0, 30)


## In-built atmospheric profiles¶

AnisoCADO uses a 35-layer description of the atmospheric turbulence to generate the PSFs. By default AnisoCADO contains 3 sets of values for the turbulence, based on the ESO-258292 document. A turbulence profiles can be set by changing the parameter profile_name of the AnalyticalScaoPsf object:

psf = anisocado.AnalyticalScaoPsf(profile_name="EsoMedian")
# or
psf.profile_name = "EsoMedian"


Alternatively we could set out own atmospheric turbulence profile if we had our own Cn2 information:

psf.layerAltitude = [0.1, 5, 12]    # km
psf.Cn2h = [0.2, 0.5, 0.3]          # relative amount of turbulence


The main 3 atmospheric profiles provided are list in the table below:

Name Conditions Seeing Zenith Distance Wind Speed Turbulence Profile

EsoQ1 Good 0.4 0 8.8 ESO Quartile 1
EsoMedian Median 0.67 30 10 ESO Median
EsoQ4 Bad 1.0 60 13 ESO Quartile 4

## Creating a field-varying PSF FITS file for SimCADO¶

AnisoCADO is able to create PSF files in the format needed by SimCADO. The function anisocado.misc.make_simcado_psf_file handles most of the nitty-gritty. We just need to provide positions relative to the centre of the field of view where the PSF should be evaluated, and the wavelength at which this should be done.

To start the example, let’s create a list of coordinates at 45 degree intervals around concentric circles at increasing radii:

# include the centre of the FOV
coords = [(0, 0)]
radii = [1, 2, 3, 5, 7, 11, 15, 23, 31]
for ang in np.arange(0, 360, 45):


To make life easier, this code in anisocado.misc:

anisocado.misc.field_positions_for_simcado_psf(radii=radii, theta=45)


Now we just need to decide which wavelengths to add. The function ´make_simcado_psf_file expects the wavelengths to be in micron [um]. Let’s take the central wavelengths for the 5 major broad-band filters of MICADO:

waves = [0.87, 1.05, 1.25, 1.65, 2.15]


We generate a fits.HDUList object by passing these to list to make_simcado_psf_file:

from anisocado import misc
hdu.writeto("my_new_fv_psf.fits")


By default AnisoCADO will create 512 x 512 pixel PSF kernels with a pixel size of 4mas. If we want to change this we can pass the parameters accepted by anisocado.psf.AnalyticalScaoPsf as kwargs:

hdu = misc.make_simcado_psf_file(coords, waves, pixelSize=0.0015, N=1024)


## Setting atmospheric parameters¶

A full list of attributes can be found in the docstring for AnalyticalScaoPsf. The ones most likely to be of interest to the casual user are given below. They can all be referenced and set in the following manner:

psf = anisocado.AnalyticalScaoPsf()
psf.profile_name = "EsoQ1"
psf.wavelength = 2.15    # um


Settable Attributes:

profile_name : str
['EsoQ1', 'EsoMedian', 'EsoQ4', 'oldEso', 'gendron']. Default: EsoMedian
Names of specific atmospheric conditions for which presets exist.
See :func:psf_utils.get_atmospheric_turbulence
N : int
[pixel] Default: 512 pixel. Side-length of the kernel array
wavelength : float
[um] Default: 2.15 um. Wavelength for which the PSF should be generated


The following parameters can be set by the user, but if they are left blank, AnisoCADO will fill them in based on the chosen profile_name. As with the normal attributes, there are many more derived attributes which the user can override. See AnalyticalScaoPsf.

Derived Attributes if not set by the user:

seeing : float
[arcsec] Default: 0.67 arcsec. Set by profile_name, if not set by user
zenDist : float
[degree] Default: 30 deg. Zenith distance. Set by profile_name, if not
set by user
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