FlatBase

class sofia_redux.spectroscopy.flats.FlatBase(filename)

Bases: object

Attributes Summary

filename

Methods Summary

adjust_guess_position(image, order[, ybuffer])	Updates guess positions and xranges via image correlation
create_order_mask(shape, orders, edgecoeffs, ...)	Create an ordermask
default_guess_position()	Sets default guess positions for each order
generate_order_mask([offset])	Generate an order mask based on xranges and edge coefficients
parse_info()

Attributes Documentation

filename

Methods Documentation

adjust_guess_position(image, order, ybuffer=3)

Updates guess positions and xranges via image correlation

The correlation occurs only in the spatial dimension (y).

Parameters:

imagearray_like of float

(nrow, ncol) Image with which to perform the correlation.

orderint

The order from the ordermask with which to perform the correlation.

ybufferint, optional

The number of pixels to buffer form the top and bottom of the array.

static create_order_mask(shape, orders, edgecoeffs, xranges)

Create an ordermask

Parameters:

shape2-tuple

(nrow, ncol) shape of the ordermask

ordersarray_like of int

(norders,) array of orders

edgecoeffsarray_like of float

(norders, 2)

xrangesarray_like of int

(norders, 2)

Returns:

ordermasknumpy.ndarray

(nrow, ncol) of int

default_guess_position()

Sets default guess positions for each order

Notes

xranges is also reset to default values

generate_order_mask(offset=0)

Generate an order mask based on xranges and edge coefficients

Parameters:

offsetint, optional

If provided, will be added to the x coordinate before generating the edge coefficient polynomials. This is intended to allow reusing edge coordinates for a shifted array in the x-direction.

Notes

No rotation is applied if we’re generating the order mask. The parameters required for order mask generation are already correct.

parse_info()