make_l2

romanisim.image.make_l2(resultants, read_pattern, read_noise=None, gain=None, flat=None, linearity=None, darkrate=None, dq=None)

Simulate an image in a filter given resultants.

This routine does idealized ramp fitting on a set of resultants.

Parameters:

resultantsnp.ndarray[nresultants, ny, nx]: Resultants array in DN
read_patternlist[list] (int): List of lists of indices of reads entering each resultant
read_noisenp.ndarray[ny, nx] (float), optional: Read noise in DN. If None, use parameters.reference_data[‘readnoise’].
gainfloat or np.ndarray, optional: Gain in electron/DN. If None, use parameters.reference_data[‘gain’].
flatnp.ndarray[ny, nx] (float), optional: Flat field to use
linearityromanisim.nonlinearity.NL object, optional: Non-linearity correction to use.
darkratenp.ndarray[ny, nx] (float), optional: Dark current rate in electron/s to subtract from ramps
dqnp.ndarray[nresultants, ny, nx] (int), optional: DQ image corresponding to resultants

Returns: