GRISMCONF

This module implements the new grism configuration described in Pirzkal and Ryan 2016 (https://www.stsci.edu/files/live/sites/www/files/home/hst/instrumentation/wfc3/documentation/instrument-science-reports-isrs/_documents/2017/WFC3-2017-01.pdf). As such, it provides the dispersion polynomials:

#!python

wavelength = DISPL(order,x0,y0,t)
dx = DISPX(order,x0,y0,t)
dy = DISPY(order,x0,y0,t)

and

#!python
t = INVDISPX(order,x0,y0,dx)
t = INVDISPY(order,x0,y0,dy)
t = INVDISPL(order,x0,y0, wavelength)

where order is configuration file specific but is usually "+1", "+2", "+3" etc..., and t is a free variable 0<t<1.

These polynomials are a generalization of the old aXe grism configuration file polynomials.

Configurations file can be found at https://github.com/npirzkal/GRISM_WFC3 for HST/WFC3 IR and https://github.com/npirzkal/GRISM_NIRCAM for JWST/NIRCAM

Installing:

Install using

pip install grismconf

or clone and install using

python setup.py install

Using:

Example 1:

We want to determine the x, y and wavelength of pixels containing the trace originating from a pixel at x0,y0 in the un-dispersed frame. We want to look at the dispersed pixels that are at location x+dxs on the dispersed image. dxs can be an array.:

#!python

import grismconf
x0 = 500.5
y0 = 600.1

# Load the Grism Configuration file
C = grismconf.Config(“NIRCAM_R.conf")
# edges of spectra (use t=0 and t=1)
dx01 = C.DISPX("+1",x0,y0,np.array([0,1]))
# Get a list of all dxs value along this trace
dxs = np.arange(dx01[0],dx01[1])
# Compute the t values corresponding to the exact offsets dxs
ts = C.INVDISPX("+1",x0,y0,dxs)
# Compute the dys values for the same pixels
dys = C.DISPY("+1",x0,y0,ts)
# Compute wavelength of each of the pixels
wavs = C.DISPL("+1",x0,y0,ts)

Example 2:

What is the dispersion in Angstrom per pixel at the left edge of the spectrum (t=0.):

#!python

C.DDISPL("+1",x0,y0,0)/C.DDISPX(order,x0,y0,0)

Example 3:

Computes what is needed to simulate the dispersion of a pixel. The free variable is t.

#!python
# We select a sensible set of values for the variable t. Since 0<t<1 corresponfs to lambda_min<lambda<lambda_max for the spectral order. 
# If we want to oversample (in the lambda direction by a factor of fac, we use:

dt = 1/(C.DISPX("+1",x0,y0,0)-C.DISPX("+1",x0,y0,1)) / fac
t = np.arange(0,1,dt)

# We can now select where the x, y and wavelength of the dispersed pixels:
#or, to generate a fake spectrum:
# By default t valid values are 0<t<1
dxs = DISPX("+1",x0,y0,t)
dys = DISPY("+1",x0,y0,t)
wavs = DISPL("+1",x0,y0,t)

How to easily compute other things: wavelength x-size of a pixel (at t): C.DDISPL(order,x0,y0,t)/C.DDISPX(order,x0,y0,t) the y-size is just: C.DDISPL(order,x0,y0,t)/C.DDISPY(order,x0,y0,t)

etc etc etc…

npirzkal / GRISMCONF