Getting started tutorial part 3: displaying exposures and source tables output by processCcd.py¶

This is part 3 of the getting started tutorial series. Before starting this tutorial, make sure you’ve completed the previous parts.

In the last tutorial we used processCcd.py to calibrate a set of raw Hyper Suprime-Cam images. Now we’ll learn how to use the LSST Science Pipelines to inspect processCcd.py’s outputs by displaying images and source catalogs in the DS9 image viewer. In the course of this tutorial you’ll be introduced to some of the LSST Science Pipelines’ Python APIs, including:

Accessing datasets with the Butler.
Displaying images in DS9 with lsst.afw.display.
Working with source catalogs using lsst.afw.table.

Setup check¶

Let’s take a moment to make sure your command line environment is set up. Run:

eups list lsst_distrib

The printed output should contain the word setup. If not, review the installation tutorials on activating the environment and setting up lsst_distrib.

Your shell’s working directory also needs to contain the Butler repository directory called DATA.

You’ll also need to download and install the DS9 image viewer.

Let’s get started.

Launch DS9 and start a Python interpreter¶

In this tutorial we will use an interactive Python session to control DS9.

If you haven’t already, launch the DS9 application.

Next, start up a Python interpreter. You can use the default Python shell (python), the IPython shell, or even run from a Jupyter Notebook. Ensure that this Python session is running from the shell where you ran setup lsst_distrib.

Creating a Butler client¶

All data in the Pipelines flows through the Butler. In the previous tutorial, processCcd.py read exposures from the Butler repository and persisted outputs back to the repository. Although the Butler data repository is a directory on the filesystem (DATA), we don’t recommend directly accessing its files. Instead, you use the Butler client from the lsst.daf.persistence module. In the Python interpreter, run:

import lsst.daf.persistence as dafPersist
butler = dafPersist.Butler(inputs='DATA/rerun/processCcdOutputs')

The Butler client reads from the data repository specified with the inputs argument. In the previous tutorial we created the processCcdOutputs rerun to isolate the outputs of the processCcd.py command line task. Reruns act like repositories, so to work with the processCcd.py outputs we specifically set inputs as the path to that rerun.

Tip

Reruns are sub-directories of the rerun directory of a root Butler data repository.

Listing available dataIds in the Butler¶

To get data from the Butler you need to know two things: the dataset type and the dataId.

Every dataset stored by the Butler has a well-defined type. Tasks read specific dataset types and output other specific dataset types. The processCcd.py command reads in raw datasets and outputs calexp, or calibrated exposure, datasets (among others). It’s calexp datasets that we will display in this tutorial.

DataIds let us reference specific instances of a dataset. On the command line we set dataIds with --id arguments, filtering by keys like visit, ccd, and filter.

Let’s use the Butler client to find what dataIds are available for the calexp dataset type:

butler.queryMetadata('calexp', ['visit', 'ccd'], dataId={'filter': 'HSC-R'})

The printed output is a list of (visit, ccd) key tuples for all dataIds where the filter key is the HSC-R band:

[(903334, 16),
 (903334, 22),
 (903334, 23),
 (903334, 100),
 (903336, 17),
 (903336, 24),
 (903338, 18),
 (903338, 25),
 (903342, 4),
 (903342, 10),
 (903342, 100),
 (903344, 0),
 (903344, 5),
 (903344, 11),
 (903346, 1),
 (903346, 6),
 (903346, 12)]

Note

The example butler.queryMetadata call is equivalent to this shell command that we used in the previous tutorial:

processCcd.py DATA --rerun processCcdOutputs --id filter=HSC-R --show data

Get an exposure through the Butler¶

Knowing a specific dataId, let’s get the dataset with the Butler client’s get method:

calexp = butler.get('calexp', dataId={'filter': 'HSC-R', 'visit': 903334, 'ccd': 23})

This calexp is an ExposureF Python object. Exposures are powerful representations of image data because they contain not only the image data, but also a variance image for uncertainty propagation, a bit mask image plane, and key-value metadata. In the next steps we’ll learn how to display an Exposure’s image and mask.

Create a display¶

To display the calexp we will use the display framework, which is imported as:

import lsst.afw.display as afwDisplay

The display framework provides a uniform API for multiple display backends, including DS9 and LSST’s Firefly viewer. For this tutorial we’ll create a display with the ds9 backend:

display = afwDisplay.getDisplay(backend='ds9')

Display the calexp (calibrated exposure)¶

Then use the display’s mtv method to view the calexp in DS9:

display.mtv(calexp)

As soon as you execute the command a single Hyper Suprime-Cam calibrated exposure, the {'filter': 'HSC-R', 'visit': 903334, 'ccd': 23} dataId, should appear in the DS9 application.

Notice that the DS9 display is filled with colorful regions. These are mask regions. Each color reflects a different mask bit that correspond to detections and different types of detector artifacts. We’ll see how to interpret these colors later, but first you’ll likely want to adjust the image display.

Improving the image display¶

The display framework gives you control over the image display to help bring out image details.

To make masked regions semi-transparent, so that underlying image features are visible, try:

display.setMaskTransparency(60)

The setMaskTransparency method’s argument can range from 0 (fully opaque) to 100 (fully transparent).

You can also control the colorbar scaling algorithm with the display’s scale method. Try an asinh stretch with the zscale algorithm for automatically selecting the white and black points:

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

Instead of an automatic algorithm like zscale (or minmax) you can explicitly provide both a minimum (black) and maximum (white) value:

display.scale("asinh", -1, 30)

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. Run:

for maskName, maskBit in mask.getMaskPlaneDict().items():
    print('{}: {}'.format(maskName, display.getMaskPlaneColor(maskName)))

As an example, this result is:

DETECTED_NEGATIVE: cyan
CROSSTALK: None
INTRP: green
DETECTED: blue
UNMASKEDNAN: None
NO_DATA: orange
BAD: red
EDGE: yellow
SUSPECT: yellow
NOT_DEBLENDED: None
CR: magenta
SAT: green

Footprints of detected sources are rendered in blue and the saturated cores of bright stars are drawn in green.

Tip

Try customizing the color of a mask plane with the Display.setMaskPlaneColor method. You can choose any X11 color. For example:

display.setMaskPlaneColor('DETECTED', 'dodgerblue')
display.mtv(calexp)

Getting the source catalog generated by processCcd.py¶

Besides the calibrated exposure (calexp), processCcd.py also creates a table of the sources it used for PSF estimation as well as astrometric and photometric calibration. The dataset type of this table is src, which you can get from the Butler:

src = butler.get('src', dataId={'filter': 'HSC-R', 'visit': 903334, 'ccd': 23})

This src dataset is a SourceTable, which is a table object from the lsst.afw.table module.

We’ll explore SourceTables more in a later tutorial, but you can check its length with Python’s len function:

print(len(src))

The columns of a table are defined in its schema. You can print out the schema to see each column’s name, data type, and description:

print(src.getSchema())

To get just the names of columns, run:

print(src.getSchema().getNames())

Given a name, you can get a column’s values as a familiar Numpy array like this:

print(src['base_PsfFlux_flux'])

Tip

If you are working in a Jupyter notebook you can see an HTML table rendering of any lsst.afw.table table object by getting an astropy.table.Table version of it:

src.asAstropy()

The returned Astropy Table is a view, not a copy, so it doesn’t waste memory.

Plotting sources on the display¶

Now let’s 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 we iterate iterate over rows in the SourceTable as usual. It’s more efficient to send a batch of updates to the display, though, so we’ll enclose the loop in a display.Buffering context, like this:

with display.Buffering():
    for s in src:
        display.dot("o", s.getX(), s.getY(), size=10, ctype='orange')

Now orange circles should appear in the DS9 window over every detected source.

Note

Notice the getX and getY methods for getting the (x,y) centroid of each source. These methods are shortcuts, using the table’s slot system, to preferred columns for a source’s centroid. Because the the src catalog contains measurements from several measurement plugins, slots are a way of easily using the pre-configured best measurements of a source.

Clearing markers¶

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

display.erase()

Selecting PSF-fitting sources to plot on the display¶

Now lets be more purposeful and use the display to understand what sources were used for PSF measurement.

The src table’s calib_psfUsed column describes whether the source was used for PSF measurement. Since columns are Numpy arrays we can iterate over rows where src['calib_psfUsed'] == True with Numpy’s boolean array indexing:

with display.Buffering():
    for s in src[src['calib_psfUsed'] == True]:
        display.dot("x", s.getX(), s.getY(), size=10, ctype='red')

Red x symbols on the display mark all stars used by PSF measurement.

Some sources might be considered as PSF candidates, but later rejected. In this statement we use a logical & (and) operator to combine boolean index arrays where both src['calib_psfCandidate'] == True and src['calib_psfUsed'] == False as well:

rejectedPsfSources = src[(src['calib_psfCandidate'] == True) &
                         (src['calib_psfUsed'] == False)]
with display.Buffering():
    for s in rejectedPsfSources:
        display.dot("+", s.getX(), s.getY(), size=10, ctype='green')

Now all green plus (+) symbols on the display mark rejected PSF measurement sources.

The display framework, as we’ve seen, is a useful facility for inspecting images and tables. This tutorial only covered the framework’s basic functionality. Explore the display framework documentation to learn how to display multiple images at once, and to work with different display backends.

Next up¶

Continue this tutorial series in part 4, where we’ll coadd these processed images into deeper mosaics.