Imaging Recipes

Stare Image

Mode:: Stare Image
Recipe class:: StareImageRecipe
Input class:: StareImageRecipeInput
Result class:: StareImageRecipeResult

The effect of recording images of the sky in a given pointing position of the TS.

Requeriments

Name	Type	Default	Meaning
`'master_bpm'`	Product	NA	Master BPM frame
`'master_bias'`	Product	NA	Master Bias frame
`'master_dark'`	Product	NA	Master Dark frame
`'nonlinearity'`	Product	[1.0, 0.0]	Master non-linearity calibration
`'master_intensity_ff'`	Product	NA	Master Intensity flat-field frame
`'extinction'`	Parameter	0.0	Mean atmospheric extinction
`'sources'`	Parameter	None	List of (x, y) coordinates to measure FWHM
`'offsets'`	Parameter	None	List of pairs of offsets
`'iterations'`	Parameter	4	Iterations of the recipe

Procedure

The block of raw frames are processed in several stages. First, the frames are corrected from bias, dark, non-linearity and intensity flat field. For these steps we use the frames in the requeriments from 'master_bpm' to 'master_intensity_ff'

Note

It is not clear the need to have a master_bias requirement, as it is only needed in one readout mode

If the parameter offsets is None, the recipe computes offset information using the WCS stored in each frame FITS header. If it is defined, the information in offsets is used instead.

The size of the result frame is computed using the sizes of the input frames and the offstes between them. New intermediate frames are created resizing the input frames.

A sky flat is computed using the input frames. Each frame is scaled acording to its mean value and then they are combined together using a sigma clipping algorithm with low and high rejection limits of 3 sigma. Each input frame is then divided by the sky flat.

Next, the sky level is estimated in each frame by obtaining the median. The sky value is then subtracted of each frame. The frames (sky-subtracted, flat-fielded and resized) are then stacked. The frames are scaled acording to its airmass and the value of 'extinction'. The algorithm used to stack frames is numina.array.combine.quantileclip() with 10% points rejected at both ends of the distribution.

Results

The result of the Recipe is an object of type StareImageRecipeResult. It contains two objects, a FrameDataProduct containing the result frame and a SourcesCatalog containing a catalog of sources.

Nodded/Beam-switched images

Mode:: Nodded/Beam-switched images
Recipe class:: NBImageRecipe
Input class:: NBImageRecipeInput
Result class:: NBImageRecipeResult

The effect of recording a series of stare images, with the same acquisition parameters, and taken by pointing the TS in cycles between two, or more, sky positions. Displacements are larger than the EMIR FOV, so the images have no common area. Used for sky subtraction.

Requeriments

Name	Type	Default	Meaning
`'master_bpm'`	Product	NA	Master BPM frame
`'master_bias'`	Product	NA	Master Bias frame
`'master_dark'`	Product	NA	Master Dark frame
`'nonlinearity'`	Product	[1.0, 0.0]	Master non-linearity calibration
`'master_intensity_ff'`	Product	NA	Master Intensity flat-field frame
`'extinction'`	Parameter	0.0	Mean atmospheric extinction
`'sources'`	Parameter	None	List of (x, y) coordinates to measure FWHM
`'offsets'`	Parameter	None	List of pairs of offsets
`'iterations'`	Parameter	4	Iterations of the recipe

Procedure

The block of raw frames contains both sky and target images. They are treated differently at some stages. Sky frames have IMGTYP = 'SKY' in their FITS headers. Target frames have IMGTYP = 'TARGET'.

All the frames are corrected from bias, dark, non-linearity and intensity flat field. For these steps we use the frames in the requeriments from 'master_bpm' to 'master_intensity_ff'

Note

It is not clear the need to have a master_bias requirement, as it is only needed in one readout mode

Then, an iterative process starts. The number of iterations is controlled by the parameter 'iterations'.

Base step

Offsets between the target frames are obtained. If the parameter offsets is None, the recipe computes offset information using the WCS stored in each frame FITS header. If it is defined, the information in offsets is used instead.

The size of the result frame is computed using the sizes of the target frames and the offsets between them. New intermediate frames are created resizing the target input frames.

A sky flat is computed using the input sky frames. Each sky frame is scaled acording to its mean value and then they are combined together using a sigma clipping algorithm with low and high rejection limits of 3 sigma. Each input target frame is then divided by the sky flat.

Next, the sky level is estimated in each frame by obtaining the median of the nearest sky image. The sky value is then subtracted of each frame. The target frames (sky-subtracted, flat-fielded and resized) are then stacked. The frames are scaled acording to its airmass and the value of 'extinction'. The algorithm used to stack frames is numina.array.combine.quantileclip() with 10% points rejected at both ends of the distribution.

Check step

In the next step, several checkings are performed in the result image.

The centroids of bright objects are compared between the input target frames and the result frame. This test allows to check if the offsets are correct and to refine the offsets.

The flux of bright objects is compared between the input target frames and the result frame. This test allows to find target frames with abnormal illumination (due to clouds, for example). The parameter 'check_photometry_levels' mark different categories of clasification of the frames acording the fraction of the median flux level of the frames. The parameter 'check_photometry_actions' allow the user to select the action to take in each category. The allowed actions are 'default'`, ``'warn' and 'reject'.

Warning

The offset-recompute routine is not yet implemented

Full reduction step

Using the latest available result image (in the first iteration, that of the base step), a segmentation mask is computed. This segmentation mask applies to target frames only.

Note

A segmentation mask for each sky frame is being considered

The sky flat is applied to the target frames.

The sky level for target frames is estimated using the median value of the nearest sky frames in the observed series. We use a number of 'sky_images' frames before and after and never separated more than 'sky_images_sep_time' minutes.

The target frames (sky-subtracted, flat-fielded and resized) are then stacked. The frames are scaled acording to its airmass and the value of 'extinction'. The algorithm used to stack frames is numina.array.combine.quantileclip() with 10% points rejected at both ends of the distribution.

This last step is repeated 'iterations' times, the segmentation mask computed from the result of the previous step.

Results

The result of the Recipe is an object of type NBImageRecipeResult. It contains two objects, a FrameDataProduct containing the result frame and a SourcesCatalog containing a catalog of sources.

Dithered images

Mode:: Dithered images
Recipe class:: DitheredImageRecipe
Input class:: DitheredImageRecipeInput
Result class:: DitheredImageRecipeResult

The effect of recording a series of stare images, with the same acquisition parameters, and taken by pointing to a number of sky positions, with separations of the order of arcsec, either by nodding the TS, tilting the TS M2 or shifting the EMIR DTU. Displacements are of the order of several pixels (even fractional). Images share the large majority of the sky positions so they can be coadded. Used for avoid cosmetic effects and/or improve the SNR. Superflat and/or supersky frames can be built from the image series.

Requeriments

Name	Type	Default	Meaning
`'master_bpm'`	Product	NA	Master BPM frame
`'master_bias'`	Product	NA	Master Bias frame
`'master_dark'`	Product	NA	Master Dark frame
`'nonlinearity'`	Product	[1.0, 0.0]	Master non-linearity calibration
`'master_intensity_ff'`	Product	NA	Master Intensity flat-field frame
`'extinction'`	Parameter	0.0	Mean atmospheric extinction
`'sources'`	Parameter	None	List of (x, y) coordinates to measure FWHM
`'offsets'`	Parameter	None	List of pairs of offsets
`'iterations'`	Parameter	4	Iterations of the recipe
`'sky_images'`	Parameter	5	Images used to estimate the background before and after current image
`'sky_images_sep_time'`	Parameter	10	Maximum separation time between consecutive sky images in minutes
`'check_photometry_levels'`	Parameter	[0.5, 0.8]	Levels to check the flux of the objects
`'chec_photometry_actions'`	Parameter	[‘warn’, ‘warn’, ‘default’]	Actions to take on images

Procedure

The block of raw frames are processed in several stages. First, the frames are corrected from bias, dark, non-linearity and intensity flat field. For these steps we use the frames in the requeriments from 'master_bpm' to 'master_intensity_ff'

Note

It is not clear the need to have a master_bias requirement, as it is only needed in one readout mode

Then, an iterative process starts. The number of iterations is controlled by the parameter 'iterations'.

Base step

Offsets between the frames are obtained. If the parameter offsets is None, the recipe computes offset information using the WCS stored in each frame FITS header. If it is defined, the information in offsets is used instead.

The size of the result frame is computed using the sizes of the input frames and the offstes between them. New intermediate frames are created resizing the input frames.

A sky flat is computed using the input frames. Each frame is scaled acording to its mean value and then they are combined together using a sigma clipping algorithm with low and high rejection limits of 3 sigma. Each input frame is then divided by the sky flat.

Next, the sky level is estimated in each frame by obtaining the median. The sky value is then subtracted of each frame. The frames (sky-subtracted, flat-fielded and resized) are then stacked. The frames are scaled acording to its airmass and the value of 'extinction'. The algorithm used to stack frames is numina.array.combine.quantileclip() with 10% points rejected at both ends of the distribution.

Check step

In the next step, several checkings are performed in the result image.

The centroids of bright objects are compared between the input frames and the result frame. This test allows to check if the offsets are correct and to refine the offsets.

The flux of bright objects is compared between the input frames and the result frame. This test allows to find frames with abnormal illumination (due to clouds, for eample). The parameter 'check_photometry_levels' mark different categories of clasification of the frames acording the fraction of the median flux level of the frames. The parameter 'check_photometry_actions' allow the user to select the action to take in each category. The allowed actions are 'default'`, ``'warn' and 'reject'.

Warning

The offset-recompute routine is not yet implemented

Full reduction step

Using the latest available result image (in the first iteration, that of the base step), a segmentation mask is computed. The segmentation mask is used to avoid objects when computing a new sky flat. With the frames corrected with the new sky flat, the sky level is estimated. For each frame, we use frames before and after in the series to compute a median sky, that is subtracted from each frame. We use a number of 'sky_images' frames before and after and never separated more than 'sky_images_sep_time' minutes.

The frames (sky-subtracted, flat-fielded and resized) are then stacked. The frames are scaled acording to its airmass and the value of 'extinction'. The algorithm used to stack frames is numina.array.combine.quantileclip() with 10% points rejected at both ends of the distribution.

This last step is repeated 'iterations' times, the segmentation mask computed from the result of the previous step.

Results

The result of the Recipe is an object of type DitheredImageRecipeResult. It contains two objects, a FrameDataProduct containing the result frame and a SourcesCatalog containing a catalog of sources.

Micro-dithered images

Mode:: Micro-dithered images
Recipe class:: MicroDitheredImageRecipe
Input class:: MicroDitheredImageRecipeInput
Result class:: MicroDitheredImageRecipeResult

The effect of recording a series of stare images, with the same acquisition parameters, and taken by pointing to a number of sky positions, with separations of the order of sub arcsecs, either by moving the either by nodding the TS, tilting the TS M2 or shifting the EMIR DTU, the latter being the most likely option. Displacements are of the order of fraction of pixels. Images share the large majority of the sky positions so they can be coadded. Used for improving the spatial resolution of the resulting images and not valid for sky or superflat images.

Requeriments

Name	Type	Default	Meaning
`'master_bpm'`	Product	NA	Master BPM frame
`'master_bias'`	Product	NA	Master Bias frame
`'master_dark'`	Product	NA	Master Dark frame
`'nonlinearity'`	Product	[1.0, 0.0]	Master non-linearity calibration
`'master_intensity_ff'`	Product	NA	Master Intensity flat-field frame
`'extinction'`	Parameter	0.0	Mean atmospheric extinction
`'sources'`	Parameter	None	List of (x, y) coordinates to measure FWHM
`'offsets'`	Parameter	None	List of pairs of offsets
`'iterations'`	Parameter	4	Iterations of the recipe
`'sky_images'`	Parameter	5	Images used to estimate the background before and after current image
`'sky_images_sep_time'`	Parameter	10	Maximum separation time between consecutive sky images in minutes
`'check_photometry_levels'`	Parameter	[0.5, 0.8]	Levels to check the flux of the objects
`'chec_photometry_actions'`	Parameter	[‘warn’, ‘warn’, ‘default’]	Actions to take on images
`'subpixelization'`	Parameter	4	Number of subdivision of each pixel side
`'window'`	Parameter	None	Region of interest

Procedure

The procedure followed by this recipe is equivalent to Dithered images. They differ in the aspects controlled by the parameters 'subpixelization' and 'window'. If 'window' is different to None, the frames are clipped to the size 'window'. Each pixel of the input frames is subdivided in 'subpixelization' x 'subpixelization' pixels.

Results

The result of the Recipe is an object of type MicroDitheredImageRecipeResult. It contains two objects, a FrameDataProduct containing the result frame and a SourcesCatalog containing a catalog of sources.