SimCADO FAQs¶

Here are some answers to known issues with SimCADO.

Work around for failing `install_noise_cube()` with Python 2.7¶

The problem lies with Python 2.7. The noise cube code is 3rd party code that only works on Python 3 and I haven’t had a chance to dig into that code yet to find the problem

Generate a noise cube in Python 3. First install python3:

$ pip install simcado
$ python3

Then create the noise cube:

>>> import simcado
>>> sim.detector.make_noise_cube(num_layers=25, filename='FPA_noise.fits', multicore=True)

Note

Your simcado/data folder can be found by printing the __pkg_dir__ variable:

>>> simcado.utils.__pkg_dir__

Copy the new noise cube into the Python 2.7 simcado/data folder.¶

By default SimCADO looks for the noise cube in its data directory - <Your Python 2.7 Directory>/lib/python/site-packages/simcado/data/:

$ cp ./FPA_noise.fits  <Your Python 2.7 Directory>/lib/python/site-packages/simcado/data/FPA_noise.fits

No access to the `simcado/data` folder?¶

If you can’t save files into the simcado/data directory (or you can’t be bothered finding it), you can use the FPA_NOISE_PATH keyword when running a simulation to point simcado to the new noise cube file:

>>> simcado.run(my_source, ..... , FPA_NOISE_PATH="<path/to/new>/FPA_noise.fits")

or if you’re using a UserCommands object to control the simulation:

>>> cmds = simcado.UserCommands()
>>> cmds["FPA_NOISE_PATH"] = "<path/to/new>/FPA_noise.fits"

Surface brightness scaling in SimCADO¶

Question

Is it true that if the array that I pass to simcado has a value of 1, then simcado will simulate exactly a SB = m mag/sq.arcsec pixel? What happens if the passed numpy array has a different pixel size. Are the counts in each pixel then scaled according to their different surface area?

To answer the question on scaling we need look at the docstring for source_from_image:

source_from_image(images, lam, spectra, plate_scale, oversample=1,
                  units="ph/s/m2", flux_threshold=0,
                  center_pixel_offset=(0, 0),
                  conserve_flux=True)

The 3 parameters of interest here are plate_scale, oversample and conserve_flux.

plate_scale is the plate scale of the image. So if we take a HAWK-I image, we have a plate_scale of 0.106 arcsec. SimCADO needs this so that it can work out how many photons are coming in per pixel in the image you provide.
oversample. If oversample stays at 1 (default), then SimCADO will generate a “point source” for each pixel - i.e. every 0.106 arcsec there will be one source emitting with the intensity of that pixel. This is sub-optimal if we want to use an extended object, as SimCADO will turn the image into a grid of point sources with a spacing equal to plate_scale. Therefore we need to over sample the image so that SimCADO makes at least 1 light source per pixel. Hence to get down to 4mas for the SimCADO wide field mode, we would need to oversample the HAWK-I image by a factor of 0.106/0.004 = 26.5. I.e. oversample=26.5.
conserve_flux. If this is True (default), then when SimCADO oversamples the image, it multiplies the new image by a scaling factor = sum(orig_image) / sum(new_image). Thus a pixel with the value 1 in the original image will now have a value=(1 / 26.5)^2. Ifconserve_flux=False`, this scaling factor is not applied. Note: The scaling doesn’t affect the spectrum associated with the image at all.

Sub-pixel accuracy with SimCADO¶

There are two ways to go about getting SimCADO to simulate at the sub-pixel level (Nov 2016, only one is working so far):

Turn on SimCADO’s oversample mode with the keyword SIM_OVERSAMPLING:
```
simcado.run(my\_src, ... , SIM\_OVERSAMPLING=4)
```

If SimCADO is using the 0.004 arcsec mode, then it will calculate everything based on a 0.001 arcsec grid. It resamples back up to 0.004 when passing the image to the detector module. Depending on the level of accuracy you need (and the computer power you have available), you can oversample as much as you’d like. Be careful with this - it uses lots of RAM.

SimCADO also has a sub-pixel mode built into the method <Source>.apply_optical_train(... ,sub_pixel=False), - (very slow if there are >100 stars, it doesn’t use FFTs) - however I’ve only done a quick tests to make sure the code works. I can’t guarantee it’s accurate though. I’ll put that on my list of things to do this week (22 Nov 2016)

I have many PSFs in a FITS Cube. How do I use just one layer¶

To extract a slice from the cube, we use astropy. Here i is the layer we want to extract:

from astropy.io import fits

f = fits.open("path/to/my/psf_cube.fits")

i = 24     # which ever layer from the cube that you want
psf = f[0].data[i, :,:]
hdr = f[0].header

hdu = fits.PrimaryHDU(data=psf, header=hdr)

hdu.header["CDELT1"] = 0.002    # whatever the plate scale of the PSF file is in arcsec
hdu.header["WAVELENG"] = 2.16   # whatever the wavelength of that layer is in micron

hdu.writeto("my_psf_layer.fits")

To use this PSF with SimCADO, we use the keyword SCOPE_PSF_FILE and pass the filename of the saved PSF slice:

simcado.run( ... , SCOPE_PSF_FILE="my_psf_layer.fits", ...)

Which filters are included in my version of SimCADO?¶

SimCADO provides a function to list all the filter curves contained in the simcado/data install directory:

>>> simcado.optics.get_filter_set()

The files containing the spectral response of the curves are located in the simcado/data install directory, which can be found by calling:

>>> simcado.__data_dir__

These files following the naming convention: TC_filter_<name>.dat. They contain two columns Wavelength [um] and Transmission [0..1]

Plotting Filter Transmission curves¶

To access a transmission curve, use the get_filter_curve() function:

>>> T_curve = simcado.optics.get_filter_curve(<FilterName>)

The returned TransmissionCurve object contains two arrays: .lam (wavelength) and .val (transmission). To access the numpy arrays:

>>> wavelength = Tcurve.lam
>>> transmission = Tcurve.val

Each TransmissionCurve object can be plotted by calling the internal method .plot(). The method uses matplotlib``s current axis (``plt.gca()), so if you are not using an iPython notebook, you will still need to call the plt.show() function afterwards. E.g.:

>>> import matplotlib.pyplot as plt
>>> T_curve.plot()
>>> plt.show()

SimCADO also has a somewhat inflexible function to plot all filter transmission curves which are in the simcado/data directory. Basically it loops over all names returned by get_filter_set() and plots them. It also applies a nice colour scheme.

>>> plot_filter_set()

I can also accept a custom list of filter names, if you don’t want to plot absolutely everything in the `simcado/data`directory (fyi, in early versions of simcado this includes many useless files - sorry)

>>> plot_filter_set(filters=("J","PaBeta","Ks"),savefig="filters.png")

What SimCADO can do?¶

Many things. Chances are it can do what you’d like, however you may need some patience, and or help from the held desk - see the contact section for who to contact if you have any questions.

What SimCADO can’t yet do?¶

Coronography, Spectroscopy

For the current version of the MICADO spectroscopy simulator see SpecCADO

https://github.com/oczoske/SpecCADO/

We are still working on incorporating SpecCADO into SimCADO, however as the SimCADO <=0.5 was primarily focused on imaging, we need to refactor the core code somewhat in order to achieve this.