Pixel Data
The most important part of an AstroData object is the pixel data.
Manipulating and interacting with pixel data is a common task in astronomy, and
astrodata provides a number of tools, as well as many familiar operations, to
make working with such data efficient and straightforward.
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.
Accessing pixel data can be done with the .data attribute. The
.data attribute is a NumPy ndarray. The .data attribute is
a property of the NDAstroData object, and it is the pixel data itself.
>>> ad = astrodata.from_file(some_fits_file_with_extensions)
>>> 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())
4194304.0
4194304.0
4194304.0
4194304.0
4194304.0
Note
Remember that extensions can be accessed by index, with index 0 being
the first extension, not the primary header unit (for FITS files).
In this example, we first access the pixels for the second extension. 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.
Arithmetic on AstroData Objects
AstroData objects support basic in-place arithmetics with these methods:
addition |
|
subtraction |
|
multiplication |
|
division |
|
In-place operations are also supported with the standard in-place assignment
operators +=, -=, *=, and /=. Normal, not in-place,
arithmetics is also possible using the standard operators, +, -, *,
and /.
When performing these operations, any variance or masks present will be
propagated forward to the resulting AstroData object (or during in-place
operations).
Simple operations
Here are a few examples of arithmetics on AstroData objects.
>>> ad = astrodata.from_file(some_fits_file_with_extensions)
>>> # Addition
>>> ad.add(50.)
<...DocTestAstroData object at ...>
>>> ad = ad + 50.
>>> ad += 50.
>>> print(ad[0].data[50,50])
151.0
>>> # Subtraction
>>> ad.subtract(50.)
<...DocTestAstroData object at ...>
>>> ad = ad - 50.
>>> ad -= 50.
>>> print(ad[0].data[50,50])
1.0
>>> # Multiplication (Using a descriptor)
>>> ad.multiply(ad.exposure_time())
<...DocTestAstroData object at ...>
>>> ad = ad * ad.exposure_time()
>>> ad *= ad.exposure_time()
>>> print(ad[0].data[50,50])
1.0
>>> # Division (Using a descriptor)
>>> ad.divide(ad.exposure_time())
<...DocTestAstroData object at ...>
>>> ad = ad / ad.exposure_time()
>>> ad /= ad.exposure_time()
>>> print(ad[0].data[50,50])
1.0
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 i, (ext, gain) in enumerate(zip(ad, ad.gain())):
... ext.multiply(gain)
... print(f"Extension {i} has been multiplied by {gain}")
<...>
Extension 0 has been multiplied by 1.5
<...>
Extension 1 has been multiplied by 1.5
<...>
Extension 2 has been multiplied by 1.5
<...>
Extension 3 has been multiplied by 1.5
<...>
Extension 4 has been multiplied by 1.5
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 i, (ext, gain) in enumerate(zip(adcopy, adcopy.gain())):
... ext.multiply(gain)
... assert ext.data is not ad[i].data
<...>
<...>
<...>
<...>
<...>
Warning
The deepcopy function is a powerful tool but it can be slow,
memory-consuming, and it can lead to unexpected results if the object being
copied contains references to other objects. It is not recommended to use
it unless you are sure you need it. In many situations, you can avoid
using it.
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_copy = deepcopy(ad)
>>> ad_copy.add(5).multiply(10).subtract(5)
<...>
>>> # means: ad = ((ad + 5) * 10) - 5
>>> # NOT: ad = ad + (5 * 10) - 5
>>> print(ad_copy[0].data[50, 50])
60.0
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_copy = deepcopy(ad)
>>> ad_copy = ad_copy + ad_copy * 3 - 40.
>>> # means: ad_copy = ad_copy + (ad_copy * 3) - 40.
>>> print(ad_copy[0].data[50, 50])
-34.0
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.
>>> print(adnew[0].data[50, 50], ad[0].data[50, 50])
-34.0 1.5
>>> adnew[0] is not ad[0]
True
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.from_file(some_fits_file_with_extensions)
>>> for (extension, gain) in zip(ad, ad.gain()):
... extension.variance = extension.data / gain
Check info(), you will see a variance plane for each
of the four extensions.
Automatic Variance Propagation
If present, any variance plane will be propagated to the resulting AstroData
object when doing arithmetics.
Note
The variance propagation assumes the data are not correlated. If the data are correlated, the variance propagation will be incorrect. In that case, the variance should be calculated from the data themselves.
Let’s look into an example.
>>> # output = x * x
>>> # var_output = var * x^2 + var * x^2
>>> ad = astrodata.from_file(some_fits_file_with_extensions)
>>> ad *= 1.5
>>> ad[1].data[50,50]
1.5
>>> ad[1].variance[50,50]
0.471
>>> adout = ad * ad
>>> adout[1].data[50,50]
2.25
>>> adout[1].variance[50,50]
0.7065
Warning
Variance must be implemented, either by setting it (above) or by including it in the data ingestion. If variance is not present, the variance propagation will not be done.
For examples of how to set the variance, see EXAMPLE.
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, Gemini bit masks use the following for bad pixels:
Meaning |
Value |
Binary |
|---|---|---|
Good pixel |
0 |
0000000 |
Bad pixel |
1 |
0000001 |
Non Linear |
2 |
0000010 |
Saturated |
4 |
0000100 |
Cosmic Ray |
8 |
0001000 |
No Data |
16 |
0010000 |
Overlap |
32 |
0100000 |
Unilluminated |
64 |
1000000 |
Note
These definitions are located in
geminidr.gemini.lookups.DQ_definitions. The are
defined as np.uint16 type integers.
So a pixel marked 10 (binary 0001010) 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 (0000001)) in one frame, saturated in another (value 4 (0000100)), and
fine in all the other the frames (value 0 (0000000)). The mask of the resulting
stack will be assigned a value of 5 (0000101) 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 follows (using a DRAGONS example):
>>> ad = astrodata.open(some_fits_file_with_mask)
>>> ad.info() # DOCTEST: +NORMALIZE_WHITESPACE
Filename: /.../some_file.fits
Tags: _DOCTEST_DATA
Pixels Extensions
Index Content Type Dimensions Format
[ 0] science NDAstroData (2048, 2048) float64
>>> ad[2].mask
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 in DS9 there is the
imexam package. 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 (.
Warning
The numdisplay package is still available for now but it is no longer
supported by STScI.
Useful tools from the NumPy, SciPy, and Astropy Packages
Scientific libraries in python provide a rich menagerie of tools for data analysis and visualization. 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 for many common tasks (or uncommon ones!).
The pixels, variance, and 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(some_fits_file_with_extensions)
>>> data = ad[0].data
>>> # Shape of the array. (equivalent to NAXIS2, NAXIS1)
>>> data.shape
(2048, 2048)
>>> # Value of a pixel at "IRAF" or DS9 coordinates (100, 50)
>>> data[49,99]
1.0
>>> # Data type
>>> data.dtype
dtype('float64')
The two most important things 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.
Warning
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(some_fits_file)
>>> data = ad[0].data
# Add some data to it to make it more interesting
>>> data += 10 * (random_number.random(data.shape) - 1.0)
# Calculate the mean, average, and median, using methods/functions.
>>> data.mean()
-5.00117...
>>> np.average(data)
-5.00117...
>>> np.median(data)
-5.00271...
As shown, both array methods like .mean() as well as numpy ufunc
functions like np.average() can be used.
See the NumPy documentation for more information and more functions that are available for use in that library.
Clipped Statistics
It is common in astronomy to apply clipping to the statistics (e.g., a clipped
average). 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
>>> stddev = data.std()
>>> mean = data.mean()
>>> clipped_mean = np.ma.masked_outside(
... data,
... mean-3*stddev,
... mean+3*stddev
... ).mean()
>>> print(
... f"standard deviation = {stddev:10.3e}",
... f"mean = {mean:10.3e}",
... f"clipped mean = {clipped_mean:10.3e}",
... sep='\n',
... ) # DOCTEST: +NORMALIZE_WHITESPACE
standard deviation = 2.887e+00
mean = -5.001e+00
clipped mean = -5.001e+00
There is no iteration in that example. It is a one-time clipping of the data specifically for this calculation.
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
>>> from astropy.stats import sigma_clip
>>> clipped_mean = np.ma.mean(sigma_clip(data, sigma=3))
>>> print(f"clipped mean = {clipped_mean:10.3e}")
clipped mean = -5.001e+00
Filters with SciPy
Another common operation is the filtering of an image, (e.g., convolusion 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.
NumPy: www.numpy.org
SciPy: www.scipy.org
Astropy: www.astropy.org
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(path_to_data)
>>> 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.
>>> # For clarity...
>>> ad = astrodata.from_file(some_fits_file_with_mask)
>>> data = ad[0].data
>>> mask = ad[0].mask
>>> breakpoint()
>>> # 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()
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, the following discussion about slixing is the same as you’ve seen throughout your Python coding experience. 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.
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))
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
Tags: ACQUISITION GEMINI GMOS IMAGE NORTH RAW SIDEREAL UNPREPARED
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
Tags: ACQUISITION GEMINI GMOS IMAGE NORTH RAW SIDEREAL UNPREPARED
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.
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.clf()
plt.plot(wavelengths, intensity)
plt.show()
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: https://matplotlib.org
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()