2.6. Pixel Data

Try it yourself

Download the data package (Try it yourself) if you wish to follow along and run the examples. Then

$ cd <path>/ad_usermanual/playground
$ python

Then import core astrodata and the Gemini astrodata configurations.

>>> import astrodata
>>> import gemini_instruments

2.6.1. Operate on Pixel Data

The pixel data are stored in the AstroData object as a list of NDAstroData objects. The NDAstroData is a subclass of Astropy’s NDData class which combines in one “package” the pixel values, the variance, and the data quality plane or mask (as well as associated meta-data). The data can be retrieved as a standard NumPy ndarray.

In the sections below, we will present several typical examples of data manipulation. But first let’s start with a quick example on how to access the pixel data.

>>> ad = astrodata.open('../playdata/N20170609S0154.fits')

>>> the_data = ad[1].data
>>> type(the_data)
<class 'numpy.ndarray'>

>>> # Loop through the extensions
>>> for ext in ad:
...     the_data = ext.data
...     print(the_data.sum())

In this example, we first access the pixels for the second extensions. Remember that in Python, list are zero-indexed, hence we access the second extension as ad[1]. The .data attribute contains a NumPy ndarray. In the for-loop, for each extension, we get the data and use the NumPy .sum() method to sum the pixel values. Anything that can be done with a ndarray can be done on AstroData pixel data.

2.6.2. Arithmetic on AstroData Objects

AstroData objects support basic in-place arithmetics with these methods:









Normal, not in-place, arithmetics is also possible using the standard operators, +, -, *, and /.

The big advantage of using AstroData to do arithmetics is that the variance and mask, if present, will be propagated through to the output AstroData object. We will explore the variance propagation in the next section and mask usage later in this chapter. Simple operations

Here are a few examples of arithmetics on AstroData objects.:

>>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits')

>>> # Addition
>>> ad.add(50.)
>>> ad = ad + 50.
>>> ad += 50.

>>> # Subtraction
>>> ad.subtract(50.)
>>> ad = ad - 50.
>>> ad -= 50.

>>> # Multiplication (Using a descriptor)
>>> ad.multiply(ad.exposure_time())
>>> ad = ad * ad.exposure_time()
>>> ad *= ad.exposure_time()

>>> # Division (Using a descriptor)
>>> ad.divide(ad.exposure_time())
>>> ad = ad / ad.exposure_time()
>>> ad /= ad.exposure_time()

When the syntax adout = adin + 1 is used, the output variable is a copy of the original. In the examples above we reassign the result back onto the original. The two other forms, ad.add() and ad += are in-place operations.

When a descriptor returns a list because the value changes for each extension, a for-loop is needed:

>>> for (ext, gain) in zip(ad, ad.gain()):
...     ext.multiply(gain)

If you want to do the above but on a new object, leaving the original unchanged, use deepcopy first.

>>> from copy import deepcopy
>>> adcopy = deepcopy(ad)
>>> for (ext, gain) in zip(adcopy, adcopy.gain()):
...     ext.multiply(gain) Operator Precedence

The AstroData arithmetics methods can be stringed together but beware that there is no operator precedence when that is done. For arithmetics that involve more than one operation, it is probably safer to use the normal Python operator syntax. Here is a little example to illustrate the difference.

>>> ad.add(5).multiply(10).subtract(5)

>>> # means:  ad = ((ad + 5) * 10) - 5
>>> # NOT: ad = ad + (5 * 10) - 5

This is because the methods modify the object in-place, one operation after the other from left to right. This also means that the original is modified.

This example applies the expected operator precedence:

>>> ad = ad + ad * 3 - 40.
>>> # means: ad = ad + (ad * 3) - 40.

If you need a copy, leaving the original untouched, which is sometimes useful you can use deepcopy or just use the normal operator and assign to a new variable.:

>>> adnew = ad + ad * 3 - 40.

2.6.3. Variance

When doing arithmetic on an AstroData object, if a variance is present it will be propagated appropriately to the output no matter which syntax you use (the methods or the Python operators). Adding a Variance Plane

In this example, we will add the poisson noise to an AstroData dataset. The data is still in ADU, therefore the poisson noise as variance is signal / gain. We want to set the variance for each of the pixel extensions.

>>> ad = astrodata.open('../playdata/N20170609S0154.fits')

>>> for (extension, gain) in zip(ad, ad.gain()):
...    extension.variance = extension.data / gain

Check ad.info(), you will see a variance plane for each of the four extensions. Automatic Variance Propagation

As mentioned before, if present, the variance plane will be propagated to the resulting AstroData object when doing arithmetics. The variance calculation assumes that the data are not correlated.

Let’s look into an example.

>>> #     output = x * x
>>> # var_output = var * x^2 + var * x^2
>>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits')

>>> ad[1].data[50,50]
>>> ad[1].variance[50,50]
>>> adout = ad * ad
>>> adout[1].data[50,50]
>>> adout[1].variance[50,50]

2.6.4. Data Quality Plane

The NDData mask stores the data quality plane. The simplest form is a True/False array of the same size at the pixel array. In Astrodata we favor a bit array that allows for additional information about why the pixel is being masked. For example at Gemini here is our bit mapping for bad pixels.



Bad pixel


Non Linear




Cosmic Ray


No Data






(These definitions are located in geminidr.gemini.lookups.DQ_definitions.)

So a pixel marked 10 in the mask, would be a “non-linear” “cosmic ray”. The AstroData masks are propagated with bitwise-OR operation. For example, let’s say that we are stacking frames. A pixel is set as bad (value 1) in one frame, saturated in another (value 4), and fine in all the other the frames (value 0). The mask of the resulting stack will be assigned a value of 5 for that pixel.

These bitmasks will work like any other NumPy True/False mask. There is a usage example below using the mask.

The mask can be accessed as follow:

>>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits')
>>> ad.info()

>>> ad[2].mask

2.6.5. Display

Since the data is stored in the AstroData object as a NumPy ndarray any tool that works on ndarray can be used. To display to DS9 there is the imexam package. The numdisplay package is still available for now but it is no longer supported by STScI. We will show how to use imexam to display and read the cursor position. Read the documentation on that tool to learn more about what else it has to offer. Displaying with imexam

Here is an example how to display pixel data to DS9 with imexam. You must start ds9 before running this example.

>>> import imexam
>>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits')

# Connect to the DS9 window (should already be opened.)
>>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0])

>>> ds9.view(ad[0].data)

# To scale "a la IRAF"
>>> ds9.view(ad[0].data)
>>> ds9.scale('zscale')

# To set the mininum and maximum scale values
>>> ds9.view(ad[0].data)
>>> ds9.scale('limits 0 2000') Retrieving cursor position with imexam

The function readcursor() can be used to retrieve cursor position in pixel coordinates. Note that it will not respond to mouse clicks, only keyboard entries are acknowledged.

When invoked, readcursor() will stop the flow of the program and wait for the user to put the cursor on top of the image and type a key. A tuple with three values will be returned: the x and y coordinates in 0-based system, and the value of the key the user hit.

>>> import imexam
>>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits')

# Connect to the DS9 window (should already be opened.)
# and display
>>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0])
>>> ds9.view(ad[0].data)
>>> ds9.scale('zscale')

>>> cursor_coo = ds9.readcursor()
>>> print(cursor_coo)

# To extract only the x,y coordinates
>>> (xcoo, ycoo) = cursor_coo[:2]
>>> print(xcoo, ycoo)

# If you are also interested in the keystroke
>>> keystroke = cursor_coo[2]
>>> print('You pressed this key: %s' % keystroke)

2.6.6. Useful tools from the NumPy, SciPy, and Astropy Packages

Like for the Display section, this section is not really specific to Astrodata but is rather a quick show-and-tell of a few things that can be done on the pixels with the big scientific packages NumPy, SciPy, and Astropy.

Those three packages are very large and rich. They have their own extensive documentation and it is highly recommend for the users to learn about what they have to offer. It might save you from re-inventing the wheel.

The pixels, the variance, and the mask are stored as NumPy ndarray’s. Let us go through some basic examples, just to get a feel for how the data in an AstroData object can be manipulated. ndarray

The data are contained in NumPy ndarray objects. Any tools that works on an ndarray can be used with Astrodata.

>>> ad = astrodata.open('../playdata/N20170609S0154.fits')

>>> data = ad[0].data

>>> # Shape of the array.  (equivalent to NAXIS2, NAXIS1)
>>> data.shape
(2112, 288)

>>> # Value of a pixel at "IRAF" or DS9 coordinates (100, 50)
>>> data[49,99]

>>> # Data type
>>> data.dtype

The two most important thing to remember for users coming from the IRAF world or the Fortran world are that the array has the y-axis in the first index, the x-axis in the second, and that the array indices are zero-indexed, not one-indexed. The examples above illustrate those two critical differences.

It is sometimes useful to know the data type of the values stored in the array. Here, the file is a raw dataset, fresh off the telescope. No operations has been done on the pixels yet. The data type of Gemini raw datasets is always “Unsigned integer (0 to 65535)”, uint16.


Beware that doing arithmetic on uint16 can lead to unexpected results. This is a NumPy behavior. If the result of an operation is higher than the range allowed by uint16, the output value will be “wrong”. The data type will not be modified to accommodate the large value. A workaround, and a safety net, is to multiply the array by 1.0 to force the conversion to a float64.

>>> a = np.array([65535], dtype='uint16')
>>> a + a
array([65534], dtype=uint16)
>>> 1.0*a + a
array([ 131070.]) Simple Numpy Statistics

A lot of functions and methods are available in NumPy to probe the array, too many to cover here, but here are a couple examples.

>>> import numpy as np

>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
>>> data = ad[0].data

>>> data.mean()
>>> np.average(data)
>>> np.median(data)

Note how mean() is called differently from the other two. mean() is a ndarray method, the others are NumPy functions. The implementation details are clearly well beyond the scope of this manual, but when looking for the tool you need, keep in mind that there are two sets of functions to look into. Duplications like .mean() and np.average() can happen, but they are not the norm. The readers are strongly encouraged to refer to the NumPy documentation to find the tool they need. Clipped Statistics

It is common in astronomy to apply clipping to the statistics, a clipped average, for example. The NumPy ma module can be used to create masks of the values to reject. In the examples below, we calculated the clipped average of the first pixel extension with a rejection threshold set to +/- 3 times the standard deviation.

Before Astropy, it was possible to do something like that with only NumPy tools, like in this example:

>>> import numpy as np

>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
>>> data = ad[0].data

>>> stddev = data.std()
>>> mean = data.mean()

>>> clipped_mean = np.ma.masked_outside(data, mean-3*stddev, mean+3*stddev).mean()

There is no iteration in that example. It is a straight one-time clipping.

For something more robust, there is an Astropy function that can help, in particular by adding an iterative process to the calculation. Here is how it is done:

>>> import numpy as np
>>> from astropy.stats import sigma_clip

>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
>>> data = ad[0].data

>>> clipped_mean = np.ma.mean(sigma_clip(data, sigma=3)) Filters with SciPy

Another common operation is the filtering of an image, for example convolving with a gaussian filter. The SciPy module ndimage.filters offers several functions for image processing. See the SciPy documentation for more information.

The example below applies a gaussian filter to the pixel array.

>>> from scipy.ndimage import filters
>>> import imexam

>>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits')
>>> data = ad[0].data

>>> # We need to prepare an array of the same size and shape as
>>> # the data array.  The result will be put in there.
>>> convolved_data = np.zeros(data.size).reshape(data.shape)

>>> # We now apply the convolution filter.
>>> sigma = 10.
>>> filters.gaussian_filter(data, sigma, output=convolved_data)

>>> # Let's visually compare the convolved image with the original
>>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0])
>>> ds9.view(data)
>>> ds9.scale('zscale')
>>> ds9.frame(2)
>>> ds9.view(convolved_data)
>>> ds9.scale('zscale')
>>> ds9.blink()
>>> # When you are convinced it's been convolved, stop the blinking.
>>> ds9.blink(blink=False)

Note that there is an Astropy way to do this convolution, with tools in astropy.convolution package. Beware that for this particular kernel we have found that the Astropy convolve function is extremely slow compared to the SciPy solution. This is because the SciPy function is optimized for a Gaussian convolution while the generic convolve function in Astropy can take in any kernel. Being able to take in any kernel is a very powerful feature, but the cost is time. The lesson here is do your research, and find the best tool for your needs. Many other tools

There are many, many other tools available out there. Here are the links to the three big projects we have featured in this section.

2.6.7. Using the Astrodata Data Quality Plane

Let us look at an example where the use of the Astrodata mask is necessary to get correct statistics. A GMOS imaging frame has large sections of unilluminated pixels; the edges are not illuminated and there are two bands between the three CCDs that represent the physical gap between the CCDs. Let us have a look at the pixels to have a better sense of the data:

>>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits')
>>> import imexam
>>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0])

>>> ds9.view(ad[0].data)
>>> ds9.scale('zscale')

See how the right and left portions of the frame are not exposed to the sky, and the 45 degree angle cuts of the four corners. The chip gaps too. If we wanted to do statistics on the whole frames, we certainly would not want to include those unilluminated areas. We would want to mask them out.

Let us have a look at the mask associated with that image:

>>> ds9.view(ad[0].mask)
>>> ds9.scale('zscale')

The bad sections are all white (pixel value > 0). There are even some illuminated pixels that have been marked as bad for a reason or another.

Let us use that mask to reject the pixels with no or bad information and do calculations only on the good pixels. For the sake of simplicity we will just do an average. This is just illustrative. We show various ways to accomplish the task; choose the one that best suits your need or that you find most readable.

>>> import numpy as np

>>> # For clarity...
>>> data = ad[0].data
>>> mask = ad[0].mask

>>> # Reject all flagged pixels and calculate the mean
>>> np.mean(data[mask == 0])
>>> np.ma.masked_array(data, mask).mean()

>>> # Reject only the pixels flagged "no_data" (bit 16)
>>> np.mean(data[(mask & 16) == 0])
>>> np.ma.masked_array(data, mask & 16).mean()
>>> np.ma.masked_where(mask & 16, data).mean()

The “long” form with np.ma.masked_* is useful if you are planning to do more than one operation on the masked array. For example:

>>> clean_data = np.ma.masked_array(data, mask)
>>> clean_data.mean()
>>> np.ma.median(clean_data)
>>> clean_data.max()

2.6.8. Manipulate Data Sections

So far we have shown examples using the entire data array. It is possible to work on sections of that array. If you are already familiar with Python, you probably already know how to do most if not all of what is in this section. For readers new to Python, and especially those coming from IRAF, there are a few things that are worth explaining.

When indexing a NumPy ndarray, the left most number refers to the highest dimension’s axis. For example, in a 2D array, the IRAF section are in (x-axis, y-axis) format, while in Python they are in (y-axis, x-axis) format. Also important to remember is that the ndarray is 0-indexed, rather than 1-indexed like in Fortran or IRAF.

Putting it all together, a pixel position (x,y) = (50,75) in IRAF or from the cursor on a DS9 frame, is accessed in Python as data[74,49]. Similarly, the IRAF section [10:20, 30:40] translate in Python to [9:20, 29:40]. Also remember that when slicing in Python, the upper limit of the slice is not included in the slice. This is why here we request 20 and 40 rather 19 and 39.

Let’s put it in action. Basic Statistics on Section

In this example, we do simple statistics on a section of the image.

>>> import numpy as np

>>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits')
>>> data = ad[0].data

>>> # Get statistics for a 25x25 pixel-wide box centered on pixel
>>> # (50,75)  (DS9 frame coordinate)
>>> xc = 49
>>> yc = 74
>>> buffer = 25
>>> (xlow, xhigh) = (xc - buffer//2, xc + buffer//2 + 1)
>>> (ylow, yhigh) = (yc - buffer//2, yc + buffer//2 + 1)
>>> # The section is [62:87, 37:62]
>>> stamp = data[ylow:yhigh, xlow:xhigh]
>>> mean = stamp.mean()
>>> median = np.median(stamp)
>>> stddev = stamp.std()
>>> minimum = stamp.min()
>>> maximum = stamp.max()

>>> print(' Mean   Median  Stddev  Min   Max\n \
... %.2f  %.2f   %.2f    %.2f  %.2f' % \
... (mean, median, stddev, minimum, maximum))

Have you noticed that the median is calculated with a function rather than a method? This is simply because the ndarray object does not have a method to calculate the median. Example - Overscan Subtraction with Trimming

Several concepts from previous sections and chapters are used in this example. The Descriptors are used to retrieve the overscan section and the data section information from the headers. Statistics are done on the NumPy ndarray representing the pixel data. Astrodata arithmetics is used to subtract the overscan level. Finally, the overscan section is trimmed off and the modified AstroData object is written to a new file on disk.

To make the example more complete, and to show that when the pixel data array is trimmed, the variance (and mask) arrays are also trimmed, let us add a variance plane to our raw data frame.

>>> ad = astrodata.open('../playdata/N20170609S0154.fits')

>>> for (extension, gain) in zip(ad, ad.gain()):
...    extension.variance = extension.data / gain

>>> # Here is how the data structure looks like before the trimming.
>>> ad.info()
Filename: ../playdata/N20170609S0154.fits

Pixels Extensions
Index  Content                  Type              Dimensions     Format
[ 0]   science                  NDAstroData       (2112, 288)    uint16
          .variance             ndarray           (2112, 288)    float64
[ 1]   science                  NDAstroData       (2112, 288)    uint16
          .variance             ndarray           (2112, 288)    float64
[ 2]   science                  NDAstroData       (2112, 288)    uint16
          .variance             ndarray           (2112, 288)    float64
[ 3]   science                  NDAstroData       (2112, 288)    uint16
          .variance             ndarray           (2112, 288)    float64

>>> # Let's operate on the first extension.
>>> #
>>> # The section descriptors return the section in a Python format
>>> # ready to use, 0-indexed.
>>> oversec = ad[0].overscan_section()
>>> datasec = ad[0].data_section()

>>> # Measure the overscan level
>>> mean_overscan = ad[0].data[oversec.y1: oversec.y2, oversec.x1: oversec.x2].mean()

>>> # Subtract the overscan level.  The variance will be propagated.
>>> ad[0].subtract(mean_overscan)

>>> # Trim the data to remove the overscan section and keep only
>>> # the data section.  Note that the WCS will be automatically
>>> # adjusted when the trimming is done.
>>> #
>>> # Here we work on the NDAstroData object to have the variance
>>> # trimmed automatically to the same size as the science array.
>>> # To reassign the cropped NDAstroData, we use the reset() method.
>>> ad[0].reset(ad[0].nddata[datasec.y1:datasec.y2, datasec.x1:datasec.x2]

>>> # Now look at the dimensions of the first extension, science
>>> # and variance.  That extension is smaller than the others.
>>> ad.info()
Filename: ../playdata/N20170609S0154.fits

Pixels Extensions
Index  Content                  Type              Dimensions     Format
[ 0]   science                  NDAstroData       (2112, 256)    float64
          .variance             ndarray           (2112, 256)    float64
[ 1]   science                  NDAstroData       (2112, 288)    uint16
          .variance             ndarray           (2112, 288)    float64
[ 2]   science                  NDAstroData       (2112, 288)    uint16
          .variance             ndarray           (2112, 288)    float64
[ 3]   science                  NDAstroData       (2112, 288)    uint16
          .variance             ndarray           (2112, 288)    float64

>>> # We can write this to a new file
>>> ad.write('partly_overscan_corrected.fits')

A new feature presented in this example is the ability to work on the NDAstroData object directly. This is particularly useful when cropping the science pixel array as one will want the variance and the mask arrays cropped exactly the same way. Taking a section of the NDAstroData object (ad[0].nddata[y1:y2, x1:x2]), instead of just the .data array, does all that for us.

To reassign the cropped NDAstroData to the extension one uses the .reset() method as shown in the example.

Of course to do the overscan correction correctly and completely, one would loop over all four extensions. But that’s the only difference.

2.6.9. Data Cubes

Reduced Integral Field Unit (IFU) data is commonly represented as a cube, a three-dimensional array. The data component of an AstroData object extension can be such a cube, and it can be manipulated and explored with NumPy, AstroPy, SciPy, imexam, like we did already in this section with 2D arrays. We can use matplotlib to plot the 1D spectra represented in the third dimension.

In Gemini IFU cubes, the first axis is the X-axis, the second, the Y-axis, and the wavelength is in the third axis. Remember that in a ndarray that order is reversed (wlen, y, x).

In the example below we “collapse” the cube along the wavelenth axis to create a “white light” image and display it. Then we plot a 1D spectrum from a given (x,y) position.

>>> import imexam
>>> import matplotlib.pyplot as plt

>>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0])

>>> adcube = astrodata.open('../playdata/gmosifu_cube.fits')
>>> adcube.info()

>>> # Sum along the wavelength axis to create a "white light" image
>>> summed_image = adcube[0].data.sum(axis=0)
>>> ds9.view(summed_image)
>>> ds9.scale('minmax')

>>> # Plot a 1-D spectrum from the spatial position (14,25).
>>> plt.plot(adcube[0].data[:,24,13])
>>> plt.show()   # might be needed, depends on matplotlibrc interactive setting

Now that is nice but it would be nicer if we could plot the x-axis in units of Angstroms instead of pixels. We use the AstroData’s WCS handler, which is based on gwcs.wcs.WCS to get the necessary information. A particularity of gwcs.wcs.WCS is that it refers to the axes in the “natural” way, (x, y, wlen) contrary to Python’s (wlen, y, x). It truly requires you to pay attention.

>>> import matplotlib.pyplot as plt

>>> adcube = astrodata.open('../playdata/gmosifu_cube.fits')

# We get the wavelength axis in Angstroms at the position we want to
# extract, x=13, y=24.
# The wcs call returns a 3-element list, the third element ([2]) contains
# the wavelength values for each pixel along the wavelength axis.

>>> length_wlen_axis = adcube[0].shape[0]   # (wlen, y, x)
>>> wavelengths = adcube[0].wcs(13, 24, range(length_wlen_axis))[2] # (x, y, wlen)

# We get the intensity along that axis
>>> intensity = adcube[0].data[:, 24, 13]   # (wlen, y, x)

# We plot
plt.plot(wavelengths, intensity)

2.6.10. Plot Data

The main plotting package in Python is matplotlib. We have used it in the previous section on data cubes to plot a spectrum. There is also the project called imexam which provides astronomy-specific tools for the exploration and measurement of data. We have also used that package above to display images to DS9.

In this section we absolutely do not aim at covering all the features of either package but rather to give a few examples that can get the readers started in their exploration of the data and of the visualization packages.

Refer to the projects web pages for full documentation. Matplotlib

With Matplotlib you have full control on your plot. You do have to do a bit for work to get it perfect though. However it can produce publication quality plots. Here we just scratch the surface of Matplotlib.

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from astropy import wcs

>>> ad_image = astrodata.open('../playdata/N20170521S0925_forStack.fits')
>>> ad_spectrum = astrodata.open('../playdata/estgsS20080220S0078.fits')

>>> # Line plot from image.  Row #1044 (y-coordinate)
>>> line_index = 1043
>>> line = ad_image[0].data[line_index, :]
>>> plt.clf()
>>> plt.plot(line)
>>> plt.show()

>>> # Column plot from image, averaging across 11 pixels around colum #327
>>> col_index = 326
>>> width = 5
>>> xlow = col_index - width
>>> xhigh = col_index + width + 1
>>> thick_column = ad_image[0].data[:, xlow:xhigh]
>>> plt.clf()
>>> plt.plot(thick_column.mean(axis=1))  # mean along the width.
>>> plt.show()
>>> plt.ylim(0, 50)     # Set the y-axis range
>>> plt.plot(thick_column.mean(axis=1))
>>> plt.show()

>>> # Contour plot for a section of an image.
>>> center = (1646, 2355)
>>> width = 15
>>> xrange = (center[1]-width//2, center[1] + width//2 + 1)
>>> yrange = (center[0]-width//2, center[0] + width//2 + 1)
>>> blob = ad_image[0].data[yrange[0]:yrange[1], xrange[0]:xrange[1]]
>>> plt.clf()
>>> plt.imshow(blob, cmap='gray', origin='lower')
>>> plt.contour(blob)
>>> plt.show()

>>> # Spectrum in pixels
>>> plt.clf()
>>> plt.plot(ad_spectrum[0].data)
>>> plt.show()

>>> # Spectrum in Angstroms
>>> spec_wcs = wcs.WCS(ad_spectrum[0].hdr)
>>> pixcoords = np.array(range(ad_spectrum[0].data.shape[0]))
>>> wlen = spec_wcs.wcs_pix2world(pixcoords, 0)[0]
>>> plt.clf()
>>> plt.plot(wlen, ad_spectrum[0].data)
>>> plt.show() imexam

For those who have used IRAF, imexam is a well-known tool. The Python imexam reproduces many of of the features of its IRAF predecesor, the interactive mode of course, but it also offers programmatic tools. One can even control DS9 from Python. As for Matplotlib, here we really just scratch the surface of what imexam has to offer.

 >>> import imexam
 >>> from imexam.imexamine import Imexamine

 >>> ad_image = astrodata.open('../playdata/N20170521S0925_forStack.fits')

 # Display the image
 >>> ds9 = imexam.connect(list(imexam.list_active_ds9())[0])
 >>> ds9.view(ad_image[0].data)
 >>> ds9.scale('zscale')

 # Run in interactive mode.  Try the various commands.
>>> ds9.imexam()

 # Use the programmatic interface
 # First initialize an Imexamine object.
 >>> plot = Imexamine()

 # Line plot from image.  Row #1044 (y-coordinate)
 >>> line_index = 1043
 >>> plot.plot_line(0, line_index, ad_image[0].data)

 # Column plot from image, averaging across 11 pixels around colum #327
 # There is no setting for this, so we have to do something similar
 # to what we did with matplotlib.
 >>> col_index = 326
 >>> width = 5
 >>> xlow = col_index - width
 >>> xhigh = col_index + width + 1
 >>> thick_column = ad_image[0].data[:, xlow:xhigh]
 >>> mean_column = thick_column.mean(axis=1)
 >>> plot.plot_column(0, 0, np.expand_dims(mean_column, 1))

 >>> # Contour plot for a section of an image.
 >>> center = (1646, 2355)  # in python coordinates
 >>> width = 15
 >>> plot.contour_pars['ncolumns'][0] = width
 >>> plot.contour_pars['nlines'][0] = width
 >>> plot.contour(center[1], center[0], ad_image[0].data)