Image Display with Firefly

Contact author: Jeff Carlin
Last verified to run: 2024-12-17
LSST Science Pipelines version: Weekly 2024_50
Container Size: medium
Targeted learning level: beginner

Description: This tutorial demonstrates the Firefly interactive interface for image data.

Skills: Using Firefly as the display interface; visualizing images and their masks; overlaying sources on images.

LSST Data Products: DP0.2 collection: '2.2i/runs/DP0.2'. Dataset types: 'calexp,' 'src,' 'deepCoadd_calexp,' 'deepCoadd_forced_src,' 'deepCoadd_ref.'

Packages: lsst.afw.display, lsst.daf.butler

Credit: This tutorial is based in part on the Firefly.ipynb notebook that is available under your home directory, in notebooks/system-test.

Get Support: Find DP0-related documentation and resources at dp0.lsst.io. Questions are welcome as new topics in the Support - Data Preview 0 Category of the Rubin Community Forum. Rubin staff will respond to all questions posted there.

1. Introduction

This notebook is a counterpart to DP0.2 tutorial notebook 03a on image display and manipulation, and is intended to demonstrate the Firefly interactive interface for viewing image data. As in the complementary "Image display" notebook, we will use the Butler to access images. We will also use the lsst.afw.display library to display images, but in this case with a different "back end" -- the interactive Firefly display tool.

1.1. Package imports

First, load the lsst.afw.display library to gain access to the image visualization routines we'd like to use, and the lsst.daf.butler library, which is used to access data products.

import lsst.afw.display as afwDisplay
from lsst.daf.butler import Butler

2. Load the data to visualize

To display an image, we must first load some data. These data have been processed with the LSST Science Pipelines, and are organized in a structure that enables us to access them through the Butler. For more information on the Butler, see lsst.daf.butler.

The DP0.2 data set contains simulated images from the LSST DESC Data Challenge 2 (DC2). These data are available in an S3 bucket called dp02. We access a single image from a specific visit (192350) and detector (175). This visit was obtained with the i-band filter; although 'band': 'i' could be added to the dataId in the cell below, it would be redundant because the combination of visit and detector already uniquely identifies the exposure.

Once we define a string that contains the data directory, we start the Butler instance using the lsst.daf.butler library and its Butler class. The Butler object is initialized with a string containing the data directory we wish to access. Running the cell may take a few moments.

With the Butler instance now generated using our data directory, we can retrieve the desired calibrated exposure by telling the Butler which filter ("band"), CCD ("detector"), and visit we wish to view. To do this, define a dictionary with the required information.

dataId = {'visit': 192350, 'detector': 175}
butler = Butler('dp02', collections='2.2i/runs/DP0.2')
calexp = butler.get('calexp', **dataId)

3. Display a calexp image

To display the calexp you will use the LSST afwDisplay framework. It provides a uniform API for multiple display backends, including DS9, matplotlib, and LSST’s Firefly viewer. The default backend is ds9, but since we are working remotely on JupyterLab we would prefer to use the web-based Firefly display. A user guide for lsst.display.firefly is available on the pipelines.lsst.io site.

3.1. Create a display

Now, create a Firefly display.

Notice: If the following cell does not cause the Firefly display to open in a new tab within the Notebook Aspect, try going to "Kernel" --> "Restart Kernel and Clear All Outputs...", and then re-running all of the cells and the cell below. If that does not work, shut down this notebook and fully log out of the RSP, and log back in again. Ensure you are using the recommended image for the Notebook Aspect and at least a medium container size. If the Firefly display still does not open, please submit a GitHub Issue.

afwDisplay.setDefaultBackend('firefly')
afw_display = afwDisplay.Display(frame=1)

In the Science Platform Notebook aspect, a Firefly viewer tab appears.

Click on the tab above that says "Firefly: slateClient..." and drag it down to the middle-right part of the JupyterLab area to create two side by side panes, one with the notebook and one with the Firefly image display.

3.2. Display the calexp (calibrated exposure)

We can now build the display and use the mtv method to view the calexp with Firefly. First display an image with mask planes and then overplot some sources.

afw_display.mtv(calexp)

As soon as you execute the command a single calibrated DC2 exposure for the {'band': 'i', 'detector': 175, 'visit': 192350} data ID should appear in the Firefly JupyterLab tab.

Notice that the image is overlaid with colorful regions. These are mask regions. Each color reflects a different mask bit that corresponds to detections and different types of detector artifacts. You’ll learn how to interpret these colors later, but first you’ll likely want to adjust the image display.

3.3. Improving the image display

The display framework gives you control over the image display to help bring out image details. The default is for masked regions to be semi-transparent, so that underlying image features are visible. The setMaskTransparency method’s argument can range from 0 (fully opaque) to 100 (fully transparent).

See the difference when mask trasparency is set to 20% (mostly opaque):

afw_display.setMaskTransparency(20)

And now set it back to 80% (mostly transparent):

afw_display.setMaskTransparency(80)

You can also control the colorbar scaling algorithm with the display’s scale method. Try an asinh stretch with explicit minimum (black) and maximum (white) values:

afw_display.scale("asinh", -5, 20)

You can also use an automatic algorithm like zscale (or minmax) to select the white and black thresholds:

afw_display.scale("asinh", "zscale")

3.4. Interpreting displayed mask colors

The display framework renders each plane of the mask in a different color (plane being a different bit in the mask). To interpret these colors you can get a dictionary of mask planes from the calexp and query the display for the colors it rendered each mask plane with. For example:

mask = calexp.getMask()
for maskName, maskBit in mask.getMaskPlaneDict().items():
    print('{}: {}'.format(maskName, afw_display.getMaskPlaneColor(maskName)))

BAD: red
CR: magenta
CROSSTALK: blue
DETECTED: blue
DETECTED_NEGATIVE: cyan
EDGE: yellow
INTRP: green
NOT_DEBLENDED: cyan
NO_DATA: orange
SAT: green
STREAK: green
SUSPECT: yellow
UNMASKEDNAN: magenta
VIGNETTED: red

3.4.1. Find mask names and colors in the Firefly interactive panel

In the Firefly viewer tab, at upper right, there are several icon buttons. Hover over each icon to see pop-up text describing the button's functionality.

Click on the button for "Manipulate overlay display...". A "Layers" pop-up window will appear that contains the mask name and its color.

3.5 Adjusting displayed masked colors

There are times when one might wish to adjust the displayed masked colors -- e.g., to highlight one or more masks more prominently, or to make make the mask colors more color-blind friendly. Here we will look at two different methods for adjusting the displayed masked colors: using lsst.afw.display commands from within the notebook, and using the interactive panel within the firefly display. We will consider each of these in turn.

3.5.1 Using lsst.afw.display commands

Let us start out using lsst.afw.display commands within the notebook itself.

First, let us save the original mask plane colors to a python dictionary so we can easily reset them to their defaults later, after working with them.

origMaskPlaneColorsDict = {}
for maskName, maskBit in mask.getMaskPlaneDict().items():
    origMaskPlaneColorsDict[maskName] = afw_display.getMaskPlaneColor(maskName)

print(origMaskPlaneColorsDict)

{'BAD': 'red', 'CR': 'magenta', 'CROSSTALK': 'blue', 'DETECTED': 'blue', 'DETECTED_NEGATIVE': 'cyan', 'EDGE': 'yellow', 'INTRP': 'green', 'NOT_DEBLENDED': 'cyan', 'NO_DATA': 'orange', 'SAT': 'green', 'STREAK': 'green', 'SUSPECT': 'yellow', 'UNMASKEDNAN': 'magenta', 'VIGNETTED': 'red'}

There are only a handful of basic colors used as default colors for the different mask planes, and some are used multiple times. Some of these mask planes are not often used and they might not all appear for a given image in the Firefly display.

With lsst.afw.display, there are many options for colors. For example, one can use any of the X11 color names, which are listed here: https://en.wikipedia.org/wiki/X11_color_names.

For example, change the color of the NOT_DEBLENDED mask plane to purple:

afw_display.setMaskPlaneColor('NOT_DEBLENDED','purple')

The change won't actually appear in the Firefly display until you rerun the mtv command:

afw_display.mtv(calexp)

One can also use hex designations for colors, e.g., this color-blind-friendly set from https://gist.github.com/thriveth/8560036 :

CB_color_cycle = ['#377eb8', '#ff7f00', '#4daf4a',
                  '#f781bf', '#a65628', '#984ea3',
                  '#999999', '#e41a1c', '#dede00']

Let's reset these 7 commonly used masked planes to the first 7 of the above color-blind-friendly set of colors:

afw_display.setMaskPlaneColor('CR',CB_color_cycle[0])
afw_display.setMaskPlaneColor('CROSSTALK',CB_color_cycle[1])
afw_display.setMaskPlaneColor('DETECTED',CB_color_cycle[2])
afw_display.setMaskPlaneColor('EDGE',CB_color_cycle[3])
afw_display.setMaskPlaneColor('INTRP',CB_color_cycle[4])
afw_display.setMaskPlaneColor('NOT_DEBLENDED',CB_color_cycle[5])
afw_display.setMaskPlaneColor('SAT',CB_color_cycle[6])

Then rerun the mtv command to see the updated mask colors:

afw_display.mtv(calexp)

Now, let's reset all of the masked planes to their original displayed colors:

for key in origMaskPlaneColorsDict:
    print(key, origMaskPlaneColorsDict[key])
    afw_display.setMaskPlaneColor(key,origMaskPlaneColorsDict[key])

BAD red
CR magenta
CROSSTALK blue
DETECTED blue
DETECTED_NEGATIVE cyan
EDGE yellow
INTRP green
NOT_DEBLENDED cyan
NO_DATA orange
SAT green
STREAK green
SUSPECT yellow
UNMASKEDNAN magenta
VIGNETTED red

And rerun the mtv command to see that the mask planes have indeed reverted to their original displayed colors:

afw_display.mtv(calexp)

Finally, one can also effectively turn given masks "off" in the Firefly display by setting their transparency to 100:

afw_display.setMaskTransparency(100, 'NOT_DEBLENDED')

And then reset it to something more opaque:

afw_display.setMaskTransparency(40, 'NOT_DEBLENDED')

3.5.2 Using the Firefly interactive panel

Click on the button for "Manipulate overlay display...". The "Layers" pop-up window provides more detailed control over the mask planes.

Turn individual planes on and off by clicking the toggles at left.

Change the color and transparency by clicking on the color square, and selecting a new color or using the transparency slider bar in the "Color Picker" pop-up window.

Delete a mask layer by clicking on the X at right.

Notice: Modifications to the displayed masked colors made via lsst.afw.display commands will be mirrored in the Firefly overlay display panel. However, changes performed in the Firefly overlay display panel might not be mirrored in the lsst.afw.display variables.

For example, changing the color of the DETECTED mask layer to purple via afw_display.setMaskPlaneColor('DETECTED','purple') will appear in the graphical panel). But changing the DETECTED mask layer to purple in the graphical panel does not cause afw_display.getMaskPlaneColor('DETECTED') in the notebook tab to also yield a value of 'purple'.

3.6. Plotting sources on the display

The measurements for all sources detected in the calexp image by the LSST science pipelines are in a dataset type src. Use the Butler to get the src catalog corresponding to the same dataId as the image we displayed:

src = butler.get('src', **dataId)

The returned object, src, is a lsst.afw.table.SourceTable object. SourceTables are explored more elsewhere, but you can do some simple investigations using common python functions. For example, check the length of the object:

len(src)

2548

View an HTML rendering of the src table by getting an astropy.table.Table version of it:

src.asAstropy()

Table length=2548

id	coord_ra	coord_dec	parent	calib_detected	calib_psf_candidate	calib_psf_used	calib_psf_reserved	deblend_nChild	deblend_deblendedAsPsf	deblend_psfCenter_x	deblend_psfCenter_y	deblend_psf_instFlux	deblend_tooManyPeaks	deblend_parentTooBig	deblend_masked	deblend_skipped	deblend_rampedTemplate	deblend_patchedTemplate	deblend_hasStrayFlux	deblend_peak_center_x	deblend_peak_center_y	deblend_peakId	deblend_nPeaks	deblend_parentNPeaks	sky_source	base_NaiveCentroid_x	base_NaiveCentroid_y	base_NaiveCentroid_flag	base_NaiveCentroid_flag_noCounts	base_NaiveCentroid_flag_edge	base_NaiveCentroid_flag_resetToPeak	base_SdssCentroid_x	slot_Centroid_x	base_SdssCentroid_y	slot_Centroid_y	base_SdssCentroid_xErr	slot_Centroid_xErr	base_SdssCentroid_yErr	slot_Centroid_yErr	base_SdssCentroid_flag	base_CircularApertureFlux_flag_badCentroid	base_GaussianFlux_flag_badCentroid	base_LocalBackground_flag_badCentroid	base_NaiveCentroid_flag_badInitialCentroid	base_PsfFlux_flag_badCentroid	base_SdssShape_flag_badCentroid	base_Variance_flag_badCentroid	ext_photometryKron_KronFlux_flag_badInitialCentroid	ext_shapeHSM_HsmPsfMomentsDebiased_flag_badCentroid	ext_shapeHSM_HsmPsfMoments_flag_badCentroid	ext_shapeHSM_HsmShapeRegauss_flag_badCentroid	ext_shapeHSM_HsmSourceMomentsRound_flag_badCentroid	ext_shapeHSM_HsmSourceMoments_flag_badCentroid	slot_Centroid_flag	base_SdssCentroid_flag_edge	base_CircularApertureFlux_flag_badCentroid_edge	base_GaussianFlux_flag_badCentroid_edge	base_LocalBackground_flag_badCentroid_edge	base_NaiveCentroid_flag_badInitialCentroid_edge	base_PsfFlux_flag_badCentroid_edge	base_SdssShape_flag_badCentroid_edge	base_Variance_flag_badCentroid_edge	ext_photometryKron_KronFlux_flag_badInitialCentroid_edge	ext_shapeHSM_HsmPsfMomentsDebiased_flag_badCentroid_edge	ext_shapeHSM_HsmPsfMoments_flag_badCentroid_edge	ext_shapeHSM_HsmShapeRegauss_flag_badCentroid_edge	ext_shapeHSM_HsmSourceMomentsRound_flag_badCentroid_edge	ext_shapeHSM_HsmSourceMoments_flag_badCentroid_edge	slot_Centroid_flag_edge	base_SdssCentroid_flag_noSecondDerivative	base_CircularApertureFlux_flag_badCentroid_noSecondDerivative	base_GaussianFlux_flag_badCentroid_noSecondDerivative	base_LocalBackground_flag_badCentroid_noSecondDerivative	base_NaiveCentroid_flag_badInitialCentroid_noSecondDerivative	base_PsfFlux_flag_badCentroid_noSecondDerivative	base_SdssShape_flag_badCentroid_noSecondDerivative	base_Variance_flag_badCentroid_noSecondDerivative	ext_photometryKron_KronFlux_flag_badInitialCentroid_noSecondDerivative	ext_shapeHSM_HsmPsfMomentsDebiased_flag_badCentroid_noSecondDerivative	ext_shapeHSM_HsmPsfMoments_flag_badCentroid_noSecondDerivative	ext_shapeHSM_HsmShapeRegauss_flag_badCentroid_noSecondDerivative	ext_shapeHSM_HsmSourceMomentsRound_flag_badCentroid_noSecondDerivative	ext_shapeHSM_HsmSourceMoments_flag_badCentroid_noSecondDerivative	slot_Centroid_flag_noSecondDerivative	base_SdssCentroid_flag_almostNoSecondDerivative	base_CircularApertureFlux_flag_badCentroid_almostNoSecondDerivative	base_GaussianFlux_flag_badCentroid_almostNoSecondDerivative	base_LocalBackground_flag_badCentroid_almostNoSecondDerivative	base_NaiveCentroid_flag_badInitialCentroid_almostNoSecondDerivative	base_PsfFlux_flag_badCentroid_almostNoSecondDerivative	base_SdssShape_flag_badCentroid_almostNoSecondDerivative	base_Variance_flag_badCentroid_almostNoSecondDerivative	ext_photometryKron_KronFlux_flag_badInitialCentroid_almostNoSecondDerivative	ext_shapeHSM_HsmPsfMomentsDebiased_flag_badCentroid_almostNoSecondDerivative	ext_shapeHSM_HsmPsfMoments_flag_badCentroid_almostNoSecondDerivative	ext_shapeHSM_HsmShapeRegauss_flag_badCentroid_almostNoSecondDerivative	ext_shapeHSM_HsmSourceMomentsRound_flag_badCentroid_almostNoSecondDerivative	ext_shapeHSM_HsmSourceMoments_flag_badCentroid_almostNoSecondDerivative	slot_Centroid_flag_almostNoSecondDerivative	base_SdssCentroid_flag_notAtMaximum	base_CircularApertureFlux_flag_badCentroid_notAtMaximum	base_GaussianFlux_flag_badCentroid_notAtMaximum	base_LocalBackground_flag_badCentroid_notAtMaximum	base_NaiveCentroid_flag_badInitialCentroid_notAtMaximum	base_PsfFlux_flag_badCentroid_notAtMaximum	base_SdssShape_flag_badCentroid_notAtMaximum	base_Variance_flag_badCentroid_notAtMaximum	ext_photometryKron_KronFlux_flag_badInitialCentroid_notAtMaximum	ext_shapeHSM_HsmPsfMomentsDebiased_flag_badCentroid_notAtMaximum	ext_shapeHSM_HsmPsfMoments_flag_badCentroid_notAtMaximum	ext_shapeHSM_HsmShapeRegauss_flag_badCentroid_notAtMaximum	ext_shapeHSM_HsmSourceMomentsRound_flag_badCentroid_notAtMaximum	ext_shapeHSM_HsmSourceMoments_flag_badCentroid_notAtMaximum	slot_Centroid_flag_notAtMaximum	base_SdssCentroid_flag_resetToPeak	base_CircularApertureFlux_flag_badCentroid_resetToPeak	base_GaussianFlux_flag_badCentroid_resetToPeak	base_LocalBackground_flag_badCentroid_resetToPeak	base_NaiveCentroid_flag_badInitialCentroid_resetToPeak	base_PsfFlux_flag_badCentroid_resetToPeak	base_SdssShape_flag_badCentroid_resetToPeak	base_Variance_flag_badCentroid_resetToPeak	ext_photometryKron_KronFlux_flag_badInitialCentroid_resetToPeak	ext_shapeHSM_HsmPsfMomentsDebiased_flag_badCentroid_resetToPeak	ext_shapeHSM_HsmPsfMoments_flag_badCentroid_resetToPeak	ext_shapeHSM_HsmShapeRegauss_flag_badCentroid_resetToPeak	ext_shapeHSM_HsmSourceMomentsRound_flag_badCentroid_resetToPeak	ext_shapeHSM_HsmSourceMoments_flag_badCentroid_resetToPeak	slot_Centroid_flag_resetToPeak	base_SdssCentroid_flag_badError	base_CircularApertureFlux_flag_badCentroid_badError	base_GaussianFlux_flag_badCentroid_badError	base_LocalBackground_flag_badCentroid_badError	base_NaiveCentroid_flag_badInitialCentroid_badError	base_PsfFlux_flag_badCentroid_badError	base_SdssShape_flag_badCentroid_badError	base_Variance_flag_badCentroid_badError	ext_photometryKron_KronFlux_flag_badInitialCentroid_badError	ext_shapeHSM_HsmPsfMomentsDebiased_flag_badCentroid_badError	ext_shapeHSM_HsmPsfMoments_flag_badCentroid_badError	ext_shapeHSM_HsmShapeRegauss_flag_badCentroid_badError	ext_shapeHSM_HsmSourceMomentsRound_flag_badCentroid_badError	ext_shapeHSM_HsmSourceMoments_flag_badCentroid_badError	slot_Centroid_flag_badError	base_Blendedness_old	base_Blendedness_raw	base_Blendedness_raw_child_instFlux	base_Blendedness_raw_parent_instFlux	base_Blendedness_abs	base_Blendedness_abs_child_instFlux	base_Blendedness_abs_parent_instFlux	base_Blendedness_raw_child_xx	base_Blendedness_raw_child_yy	base_Blendedness_raw_child_xy	base_Blendedness_raw_parent_xx	base_Blendedness_raw_parent_yy	base_Blendedness_raw_parent_xy	base_Blendedness_abs_child_xx	base_Blendedness_abs_child_yy	base_Blendedness_abs_child_xy	base_Blendedness_abs_parent_xx	base_Blendedness_abs_parent_yy	base_Blendedness_abs_parent_xy	base_Blendedness_flag	base_Blendedness_flag_noCentroid	base_Blendedness_flag_noShape	base_FPPosition_x	base_FPPosition_y	base_FPPosition_flag	base_FPPosition_missingDetector_flag	base_Jacobian_value	base_Jacobian_flag	base_SdssShape_xx	base_SdssShape_yy	base_SdssShape_xy	base_SdssShape_xxErr	base_SdssShape_yyErr	base_SdssShape_xyErr	base_SdssShape_x	base_SdssShape_y	base_SdssShape_instFlux	base_SdssShape_instFluxErr	base_SdssShape_psf_xx	base_SdssShape_psf_yy	base_SdssShape_psf_xy	base_SdssShape_instFlux_xx_Cov	base_SdssShape_instFlux_yy_Cov	base_SdssShape_instFlux_xy_Cov	base_SdssShape_flag	base_SdssShape_flag_unweightedBad	base_SdssShape_flag_unweighted	base_SdssShape_flag_shift	base_SdssShape_flag_maxIter	base_SdssShape_flag_psf	ext_shapeHSM_HsmPsfMoments_x	slot_PsfShape_x	ext_shapeHSM_HsmPsfMoments_y	slot_PsfShape_y	ext_shapeHSM_HsmPsfMoments_xx	slot_PsfShape_xx	ext_shapeHSM_HsmPsfMoments_yy	slot_PsfShape_yy	ext_shapeHSM_HsmPsfMoments_xy	slot_PsfShape_xy	ext_shapeHSM_HsmPsfMoments_flag	slot_PsfShape_flag	ext_shapeHSM_HsmPsfMoments_flag_no_pixels	slot_PsfShape_flag_no_pixels	ext_shapeHSM_HsmPsfMoments_flag_not_contained	slot_PsfShape_flag_not_contained	ext_shapeHSM_HsmPsfMoments_flag_parent_source	slot_PsfShape_flag_parent_source	ext_shapeHSM_HsmPsfMoments_flag_galsim	slot_PsfShape_flag_galsim	ext_shapeHSM_HsmPsfMoments_flag_edge	slot_PsfShape_flag_edge	ext_shapeHSM_HsmShapeRegauss_e1	ext_shapeHSM_HsmShapeRegauss_e2	ext_shapeHSM_HsmShapeRegauss_sigma	ext_shapeHSM_HsmShapeRegauss_resolution	ext_shapeHSM_HsmShapeRegauss_flag	ext_shapeHSM_HsmShapeRegauss_flag_no_pixels	ext_shapeHSM_HsmShapeRegauss_flag_not_contained	ext_shapeHSM_HsmShapeRegauss_flag_parent_source	ext_shapeHSM_HsmShapeRegauss_flag_galsim	ext_shapeHSM_HsmSourceMoments_x	slot_Shape_x	ext_shapeHSM_HsmSourceMoments_y	slot_Shape_y	ext_shapeHSM_HsmSourceMoments_xx	slot_Shape_xx	ext_shapeHSM_HsmSourceMoments_yy	slot_Shape_yy	ext_shapeHSM_HsmSourceMoments_xy	slot_Shape_xy	ext_shapeHSM_HsmSourceMoments_flag	base_GaussianFlux_flag_badShape	slot_Shape_flag	ext_shapeHSM_HsmSourceMoments_flag_no_pixels	base_GaussianFlux_flag_badShape_no_pixels	slot_Shape_flag_no_pixels	ext_shapeHSM_HsmSourceMoments_flag_not_contained	base_GaussianFlux_flag_badShape_not_contained	slot_Shape_flag_not_contained	ext_shapeHSM_HsmSourceMoments_flag_parent_source	base_GaussianFlux_flag_badShape_parent_source	slot_Shape_flag_parent_source	ext_shapeHSM_HsmSourceMoments_flag_galsim	base_GaussianFlux_flag_badShape_galsim	slot_Shape_flag_galsim	ext_shapeHSM_HsmSourceMoments_flag_edge	base_GaussianFlux_flag_badShape_edge	slot_Shape_flag_edge	ext_shapeHSM_HsmSourceMomentsRound_x	slot_ShapeRound_x	ext_shapeHSM_HsmSourceMomentsRound_y	slot_ShapeRound_y	ext_shapeHSM_HsmSourceMomentsRound_xx	slot_ShapeRound_xx	ext_shapeHSM_HsmSourceMomentsRound_yy	slot_ShapeRound_yy	ext_shapeHSM_HsmSourceMomentsRound_xy	slot_ShapeRound_xy	ext_shapeHSM_HsmSourceMomentsRound_flag	slot_ShapeRound_flag	ext_shapeHSM_HsmSourceMomentsRound_flag_no_pixels	slot_ShapeRound_flag_no_pixels	ext_shapeHSM_HsmSourceMomentsRound_flag_not_contained	slot_ShapeRound_flag_not_contained	ext_shapeHSM_HsmSourceMomentsRound_flag_parent_source	slot_ShapeRound_flag_parent_source	ext_shapeHSM_HsmSourceMomentsRound_flag_galsim	slot_ShapeRound_flag_galsim	ext_shapeHSM_HsmSourceMomentsRound_flag_edge	slot_ShapeRound_flag_edge	ext_shapeHSM_HsmSourceMomentsRound_Flux	slot_ShapeRound_Flux	base_CircularApertureFlux_3_0_instFlux	base_CircularApertureFlux_3_0_instFluxErr	base_CircularApertureFlux_3_0_flag	base_CircularApertureFlux_3_0_flag_apertureTruncated	base_CircularApertureFlux_3_0_flag_sincCoeffsTruncated	base_CircularApertureFlux_4_5_instFlux	base_CircularApertureFlux_4_5_instFluxErr	base_CircularApertureFlux_4_5_flag	base_CircularApertureFlux_4_5_flag_apertureTruncated	base_CircularApertureFlux_4_5_flag_sincCoeffsTruncated	base_CircularApertureFlux_6_0_instFlux	base_CircularApertureFlux_6_0_instFluxErr	base_CircularApertureFlux_6_0_flag	base_CircularApertureFlux_6_0_flag_apertureTruncated	base_CircularApertureFlux_6_0_flag_sincCoeffsTruncated	base_CircularApertureFlux_9_0_instFlux	base_CircularApertureFlux_9_0_instFluxErr	base_CircularApertureFlux_9_0_flag	base_CircularApertureFlux_9_0_flag_apertureTruncated	base_CircularApertureFlux_9_0_flag_sincCoeffsTruncated	base_CircularApertureFlux_12_0_instFlux	slot_ApFlux_instFlux	slot_CalibFlux_instFlux	base_CircularApertureFlux_12_0_instFluxErr	slot_ApFlux_instFluxErr	slot_CalibFlux_instFluxErr	base_CircularApertureFlux_12_0_flag	slot_ApFlux_flag	slot_CalibFlux_flag	base_CircularApertureFlux_12_0_flag_apertureTruncated	slot_ApFlux_flag_apertureTruncated	slot_CalibFlux_flag_apertureTruncated	base_CircularApertureFlux_12_0_flag_sincCoeffsTruncated	slot_ApFlux_flag_sincCoeffsTruncated	slot_CalibFlux_flag_sincCoeffsTruncated	base_CircularApertureFlux_17_0_instFlux	base_CircularApertureFlux_17_0_instFluxErr	base_CircularApertureFlux_17_0_flag	base_CircularApertureFlux_17_0_flag_apertureTruncated	base_CircularApertureFlux_25_0_instFlux	base_CircularApertureFlux_25_0_instFluxErr	base_CircularApertureFlux_25_0_flag	base_CircularApertureFlux_25_0_flag_apertureTruncated	base_CircularApertureFlux_35_0_instFlux	base_CircularApertureFlux_35_0_instFluxErr	base_CircularApertureFlux_35_0_flag	base_CircularApertureFlux_35_0_flag_apertureTruncated	base_CircularApertureFlux_50_0_instFlux	base_CircularApertureFlux_50_0_instFluxErr	base_CircularApertureFlux_50_0_flag	base_CircularApertureFlux_50_0_flag_apertureTruncated	base_CircularApertureFlux_70_0_instFlux	base_CircularApertureFlux_70_0_instFluxErr	base_CircularApertureFlux_70_0_flag	base_CircularApertureFlux_70_0_flag_apertureTruncated	base_GaussianFlux_instFlux	slot_GaussianFlux_instFlux	slot_ModelFlux_instFlux	base_GaussianFlux_instFluxErr	slot_GaussianFlux_instFluxErr	slot_ModelFlux_instFluxErr	base_GaussianFlux_flag	slot_GaussianFlux_flag	slot_ModelFlux_flag	base_LocalBackground_instFlux	base_LocalBackground_instFluxErr	base_LocalBackground_flag	base_LocalBackground_flag_noGoodPixels	base_LocalBackground_flag_noPsf	base_LocalPhotoCalib_flag	base_LocalPhotoCalib	base_LocalPhotoCalibErr	base_LocalWcs_flag	base_LocalWcs_CDMatrix_1_1	base_LocalWcs_CDMatrix_1_2	base_LocalWcs_CDMatrix_2_1	base_LocalWcs_CDMatrix_2_2	base_PixelFlags_flag	base_PixelFlags_flag_offimage	base_PixelFlags_flag_edge	base_PixelFlags_flag_interpolated	base_PixelFlags_flag_saturated	base_PixelFlags_flag_cr	base_PixelFlags_flag_bad	base_PixelFlags_flag_suspect	base_PixelFlags_flag_interpolatedCenter	base_PixelFlags_flag_saturatedCenter	base_PixelFlags_flag_crCenter	base_PixelFlags_flag_suspectCenter	base_PsfFlux_instFlux	slot_PsfFlux_instFlux	base_PsfFlux_instFluxErr	slot_PsfFlux_instFluxErr	base_PsfFlux_area	slot_PsfFlux_area	base_PsfFlux_chi2	slot_PsfFlux_chi2	base_PsfFlux_npixels	slot_PsfFlux_npixels	base_PsfFlux_flag	slot_PsfFlux_flag	base_PsfFlux_flag_noGoodPixels	slot_PsfFlux_flag_noGoodPixels	base_PsfFlux_flag_edge	slot_PsfFlux_flag_edge	base_Variance_flag	base_Variance_value	base_Variance_flag_emptyFootprint	ext_photometryKron_KronFlux_instFlux	ext_photometryKron_KronFlux_instFluxErr	ext_photometryKron_KronFlux_radius	ext_photometryKron_KronFlux_radius_for_radius	ext_photometryKron_KronFlux_psf_radius	ext_photometryKron_KronFlux_flag	ext_photometryKron_KronFlux_flag_edge	ext_photometryKron_KronFlux_flag_bad_shape_no_psf	ext_photometryKron_KronFlux_flag_no_minimum_radius	ext_photometryKron_KronFlux_flag_no_fallback_radius	ext_photometryKron_KronFlux_flag_bad_radius	ext_photometryKron_KronFlux_flag_used_minimum_radius	ext_photometryKron_KronFlux_flag_used_psf_radius	ext_photometryKron_KronFlux_flag_small_radius	ext_photometryKron_KronFlux_flag_bad_shape	ext_shapeHSM_HsmPsfMomentsDebiased_x	slot_PsfShapeDebiased_x	ext_shapeHSM_HsmPsfMomentsDebiased_y	slot_PsfShapeDebiased_y	ext_shapeHSM_HsmPsfMomentsDebiased_xx	slot_PsfShapeDebiased_xx	ext_shapeHSM_HsmPsfMomentsDebiased_yy	slot_PsfShapeDebiased_yy	ext_shapeHSM_HsmPsfMomentsDebiased_xy	slot_PsfShapeDebiased_xy	ext_shapeHSM_HsmPsfMomentsDebiased_flag	slot_PsfShapeDebiased_flag	ext_shapeHSM_HsmPsfMomentsDebiased_flag_no_pixels	slot_PsfShapeDebiased_flag_no_pixels	ext_shapeHSM_HsmPsfMomentsDebiased_flag_not_contained	slot_PsfShapeDebiased_flag_not_contained	ext_shapeHSM_HsmPsfMomentsDebiased_flag_parent_source	slot_PsfShapeDebiased_flag_parent_source	ext_shapeHSM_HsmPsfMomentsDebiased_flag_galsim	slot_PsfShapeDebiased_flag_galsim	ext_shapeHSM_HsmPsfMomentsDebiased_flag_edge	slot_PsfShapeDebiased_flag_edge	detect_isPrimary	detect_isDeblendedSource	detect_fromBlend	detect_isIsolated	base_GaussianFlux_apCorr	slot_GaussianFlux_apCorr	slot_ModelFlux_apCorr	base_GaussianFlux_apCorrErr	slot_GaussianFlux_apCorrErr	slot_ModelFlux_apCorrErr	base_GaussianFlux_flag_apCorr	slot_GaussianFlux_flag_apCorr	slot_ModelFlux_flag_apCorr	base_PsfFlux_apCorr	slot_PsfFlux_apCorr	base_PsfFlux_apCorrErr	slot_PsfFlux_apCorrErr	base_PsfFlux_flag_apCorr	slot_PsfFlux_flag_apCorr	ext_photometryKron_KronFlux_apCorr	ext_photometryKron_KronFlux_apCorrErr	ext_photometryKron_KronFlux_flag_apCorr	base_ClassificationExtendedness_value	base_ClassificationExtendedness_flag	base_FootprintArea_value	calib_astrometry_used	calib_photometry_used	calib_photometry_reserved
	rad	rad								pix	pix	ct								pix	pix					pix	pix					pix	pix	pix	pix	pix	pix	pix	pix																																																																																																												ct	ct		ct	ct	pix2	pix2	pix2	pix2	pix2	pix2	pix2	pix2	pix2	pix2	pix2	pix2				mm	mm					pix2	pix2	pix2	pix2	pix2	pix2	pix	pix	ct	ct	pix2	pix2	pix2	pix2 ct	pix2 ct	pix2 ct							pix	pix	pix	pix	pix2	pix2	pix2	pix2	pix2	pix2																						pix	pix	pix	pix	pix2	pix2	pix2	pix2	pix2	pix2																			pix	pix	pix	pix	pix2	pix2	pix2	pix2	pix2	pix2															ct	ct				ct	ct				ct	ct				ct	ct				ct	ct	ct	ct	ct	ct										ct	ct			ct	ct			ct	ct			ct	ct			ct	ct			ct	ct	ct	ct	ct	ct				ct	ct																								ct	ct	ct	ct	pix	pix			pix	pix										ct	ct														pix	pix	pix	pix	pix2	pix2	pix2	pix2	pix2	pix2																																					pix
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Now overplot sources from the src table onto the image display using the Display’s dot method for plotting markers. Display.dot plots markers individually, so you’ll need to iterate over rows in the SourceTable. It is more efficient to send a batch of updates to the display, so enclose the loop in a display.Buffering context, like this:

with afw_display.Buffering():
    for record in src:
        afw_display.dot('o', record.getX(), record.getY(), size=20, ctype='orange')

3.7. Clearing markers

Display.dot always adds new markers to the display. To clear the display of all markers, use the erase method:

afw_display.erase()

4. Display a coadd image

"Coadds" are the images made by combining multiple overlapping calexps to create a deeper image. Let's look at one of them (in fact, the same coadd that was examined in the related "Image Display" notebook). The Butler dataset type is called a deepCoadd_calexp.

dataId_coadd = {'tract': 4431, 'patch': 17, 'band': 'i'}
coadd_calexp = butler.get('deepCoadd_calexp', **dataId_coadd)

We will also grab the measurements for sources in this coadd (called deepCoadd_forced_src because it is "forced" photometry at the positions of all detected objects), and the "reference table" that contains merged information about all detected objects from all filters.

forced_src = butler.get('deepCoadd_forced_src', **dataId_coadd)
refTable = butler.get('deepCoadd_ref', **dataId_coadd)

afw_display.mtv(coadd_calexp)

That includes a lot of mask information! Use the layers functionality in the Firefly window to turn off "INEXACT_PSF", "REJECTED", and "SENSOR_EDGE" to reclaim your view of most of the pixel data.

Optionally, set the transparency of all mask layers to 100%.

afw_display.setMaskTransparency(100)

4.1. Plotting sources on the displayed coadd

As we did for the calexp, let's display sources on top of the deepCoadd_calexp image. We will apply three selection criteria:

1. Spatially select objects that are near the bottom left corner of the image (at X<1000 and Y<1000, where X and Y are the pixel values. (This is just so that the overplotting will go faster.)

2. Use the detect_isPrimary flag to select non-duplicate objects that have been deblended (see this section of the pipelines tutorial for details).

3. Use one of the flags from the reference table that tells you whether a source was measured to be "extended" (like a galaxy). The base_ClassificationExtendedness_value is set to 1 for extended sources (galaxies) and 0 for point sources (stars). Again, see the tutorial section linked above for more about this flag and an example of its usage.

Note that if you want to see all of the columns in the forced_src or refTable catalogs, you can use the method "forced_src.schema" or "refTable.schema".

Notice: Because a "patch" is part of a larger "tract", the (X, Y) coordinates of the lower-left corner are not (0, 0). We need to get the coordinates of that corner and subtract them off, which is what the first few lines of the next code cell does.

xy0 = coadd_calexp.getXY0()
xPos = refTable["base_SdssCentroid_x"] - xy0.getX()
yPos = refTable["base_SdssCentroid_y"] - xy0.getY()

boxSelect = (xPos < 1000) & (yPos < 1000)

isPrimary = refTable['detect_isPrimary']

isStellar = refTable['base_ClassificationExtendedness_value'] < 1.

Display stars in the lower-left corner with orange circles.

with afw_display.Buffering():
    for record in forced_src[boxSelect & isPrimary & isStellar]:
        afw_display.dot('o', record.getX(), record.getY(), size=20, ctype='orange')

Display galaxies in the lower-left corner (really, just anything that is "extended") with blue circles

with afw_display.Buffering():
    for record in forced_src[boxSelect & isPrimary & ~isStellar]:
        afw_display.dot('o', record.getX(), record.getY(), size=20, ctype='blue')

Use the magnifying glass icon to zoom in on the lower-left corner where the objects are marked, and inspect the image. Use the color palate and histogram icons to change the color bar and pixel scaling to examine faint sources within circles that appear empty.

5. Exercises for the learner

This tutorial was a basic introduction to displaying and manipulating images with Firefly.

1. Change the colors of the mask plane, or the flux scaling of the image display.
2. Explore a different deepCoadd (e.g., the galaxy cluster used in the introductory notebook).
3. Identify different source populations to overplot (e.g., faintest stars, inclined galaxies).

Enjoy exploring the DP0 images and catalogs!

5.1. Review additional documentation

If you would like more information on lsst.afw.display, please have a look at the following websites:

• Firefly user guide

• Getting Started on Image Display (pipelines.lsst.io)

• afw.display Doxygen website

• afw.display GitHub website