snr¶

simcado.simulation.snr(exptimes, mags, filter_name='Ks', cmds=None, **kwargs)[source]¶

Returns the signal-to-noise ratio(s) for given exposure times and magnitudes

Each time this runs, simcado runs a full simulation on a grid of stars. Therefore if you are interested in the SNR for many difference expoure times and a range of magnitudes, it is faster to pass all of them at once to this function. See the example section below.

Parameters

exptimesfloat, list: [s] A single or multiple exposure times
magsfloat, list: [mag] A single or multiple magnitudes
filter_namestr, optional: The name of the filter to be used - See get_filter_set() The default is “Ks”
cmdsUserCommands object, optional: Extra commands to be passed to simcado.simulation.run().

Returns

snr_returnlist: A list of SNR values for each exposure time and each magnitude

Examples

A basic example of wanting the SNR for a Ks=24 star in a 1 hr observation

>>> snr(exptimes=3600, mags=24)
[72.69760133863036]

However this is slow because it runs a full simulation. Hence it is better to do more at once If we want the SNR for the range of magnitudes J=[15, 20, 25, 30] for a 1 hr observation:

>>> snr(exptimes=3600, mags=[15,20,25,30], filter_name="J")
[array([  2.35125027e+04,   2.74921916e+03,   8.97552604e+01,
  8.18183097e-01])]

Now if we were interested in different exposure times, say 10 minutes and 5 hours, for a 24th magnitude star in the narrow band Br$gamma$ filter:

>>> # Chekc the name of the Brackett Gamma filter
>>> [name for name in simcado.optics.get_filter_set() if "Br" in name]
['BrGamma']
>>> snr(exptimes=[600, 18000], mags=24, filter_name="BrGamma")
[8.016218764390803, 42.71569256185457]