SLiPy

A Spectroscopy and astrophysics Library for Python 3

This Python package is an expanding code base for doing computational astronomy, particularly spectroscopy. It contains both a Spectrum class for handling spectra as objects (with +, -, *, /, etc... operations defined) and a growing suite of analysis tools.

Dependencies: Python 3.x, astropy, matplotlib, numpy, scipy

Quick note: the subpackage astrolibpy was not developed by me. It was coded by Sergey Koposov (@segasai) at Cambridge (then at least). I found it useful for performing velocity corrections on my spectroscopic data. I've modified several modules such that it can be imported and used in Python 3.x. See his README file.

Contents

SLiPy is split into several components. The principle component is the subpackage SLiPy itself, which contains all the relevant functionality. Further, Data is a package I'm working on that will provide an API for searching astronomical data archives in a simple way. The other two subpackages Framework and astrolibpy are of utility to the project but not necessarily intended for export. As stated previously, astrolibpy was not developed by me, only modified. I'm not going to document it's usage here. Its name is unfortunate for me as it is a bit over done with the convention I was already using, but for consistency I will keep it as it was from the author.

The following modules are elevated to the package level and are available to import:

Installation

To install SLiPy, there is no setup procedure. Simply download the package, either by clicking on the download link for a tar or zip archive or by cloning it.

Extract it's contents to wherever you like in a directory (ostensibly named slipy, but actually you can call this library whatever you want as well because all the imports are relative). Then add the parent directory to your PYTHONPATH if it isn't already. For example:

$ cd
$ git clone http://github.com/glentner/slipy
$ echo "export PYTHONPATH=$PYTHONPATH:~" >> ~/.bashrc

And your ready to go!

Exceptions

SLiPy attempts to catch all foreseeable exceptions and re-throw them under a common handle with a human readable message. There is a unique exception class for every module, all derived from a common SlipyError. The naming convention is for a module's exception to be named after the module with the addition of the word Error. So the Fits module will throw a FitsError. These are meant to handle erros internally. The user need not worry about these in an interactive session; however, inside of scipts you might catch SlipyError. For finer control, catch the individual exceptions (e.g., Fits.FitsError).

Contribute

If you use SLiPy or have your own code related to spectroscopy or computing for astronomy and think it would be a useful addition (or you find a bug/mistake) I'm more than open to suggested contributions/additions.

Author(s)

Geoffrey Lentner, B.S.
Graduate Research Assistant
Department of Physics & Astronomy
University of Louisville

Significant intellectual contributions have been made by my thesis advisor, specifically in terms of the science behind much of this package.

James Lauroesch, Ph.D.
Associate Professor
Department of Physics & Astronomy
University of Louisville

Acknowledge SLiPy

If you have made use of SLiPy in your project/research, you can acknowledge your use in the following ways:

Publications
This research has made use of SLiPy, an open source spectroscopy and astrophysics library for Python 3 (G. Lentner, 2015).

Projects
If your code either makes use of or borrows from SLiPy, a good way to reference this is with a shield in your README file.

The above badge is generated using the following snippet

[![SLiPy](http://img.shields.io/badge/running-SLiPy-green.svg?style=flat)](http://glentner.github.io/SLiPy)

Documentation

Spectrum

Objects for representing astronomical data. Currently, this includes the Spectrum class and it's helper function WaveVector.

• WaveVector ( rpix, rval, delt, npix ):

  Construct numpy array of wavelength values where rpix is the reference pixel index, rval is the wavelength at reference pixel, delt is the resolutions (delta lambda), and npix is the length of desired array.

• class Spectrum ( *args, **kwargs ):

  The Spectrum object is a container class for a data array and its corresponding wavelength array.

  There are a few ways to create a Spectrum. If a single string argument is given, it is taken as a file name and used to read in data from a FITS file. With the keyword argument wavecal set as True (the default case), elements are read from the header to create a corresponding wavelength array to go with the data.

  Options	Defaults	Descriptions
  wavecal	True	fit wavelength vector to data
  crpix1	'crpix1'	reference pixel header keyword
  crval1	'crval1'	value at reference pixel
  cdelt1	'cdelt1'	resolution (delta lambda)
  xunits	'Angstrom'	units for wavelength array
  yunits	''	units for data array

  
  If an array-like object is given, it is converted to a numpy array and taken as the spectrum data. A wavelength array will be generated that is simply an index (pixel) count. But if a second argument is provided, it will serve as the wavelength array. These must be equal in length however.

  Units will be imposed for these arrays. When initialized from a file, the default units are Angstrom and dimensionless_unscaled for the wavelength and data arrays respectively. Alternatives can be applied by providing the keyword arguments xunits and yunits. If initialized via an array-like object, dimensionless_unscaled will only be applied if no units are detected. If no wavelength array is provided, the generated wavelength array will have pixel units. Units are again only applied if none are detected for the given array.

  Addition, subtraction, multiplication, and division (including in-place operations, e.g., '+=') are defined for both spectrum on spectrum operations as well as scalar operations. When two spectra are operated on, the LHS spectrum takes precedent. One of the spectra must be contained within the other (i.e., their wavelength domain is either equal to or entirely interior to the other). The outer spectrum is resampled onto the inner spectrum's pixel space and the operation is applied element-wise. To state it briefly, only the affected region of the LHS spectrum is operated on. This makes units dangerous for multiplication and division. Only in the case of multiplying/dividing spectra of equivilent wavelength arrays will the units be properly applied. Otherwise, the RHS units will be ignored. Considering the physical context within which it makes sense to apply these operations to spectra this is justified; the data will have dimensionless units in all likelihood. For scalar addition and subtraction, if no units are given the units are implied to be the same as that of the data.

  The comparison operations ( >, <, >=, <=, ==, !=, &, ^, | ) are defined to return a binary Spectrum of dimensionless 1's and 0's. The same convention applies as above. Either the LHS or RHS must be contained within the other, and the LHS is compared on the overlapping regions. Data outside this overlapping region is returned as False.

  The shift operations ('>>' and '<<') are defined to mean a shift in the spectrum. The addition and subtraction operate on the data. These operations apply to the wave array. Spectrum << 2 * u.Angstrom says to blue shift the spectrum by 2 Angstroms. If the RHS is a dimensionless number, the wavelength units are implied. Also, one can shift a spectrum by another spectrum (e.g., spectrumA >> spectrumB), where the wave arrays would be operated on only. In these cases, they should have the same number of pixels! Finally, to get a variable shift across the spectrum without creating a whole spectrum with junk data, you can shift using a numpy array (also of equal length). If no units are detected, the wavelength units will be implied.

  Other operations:

  # The wavelength domain of A is either equal to or contained by B.
  # Returns True or False
  SpectrumA in SpectrumB

  # Returns number of pixels
  len(Spectrum)

  # Calls str() on member arrays, (e.g., print(Spectrum))
  str(Spectrum)

  Also, you can access the spectrum data via "index" notation. The behavior is not the same though. See the below interactive demonstration.

  In [1]: from slipy import Spectrum \
     ...: from astropy import units as u \
     ...: import numpy as np
  
  In [2]: x = np.linspace(-2*np.pi, 2*np.pi, 25) # -2pi < x < 2pi
  
  In [3]: y = np.sin( np.pi * x ) / (np.pi * x)   # sinc(pi x)
  
  In [4]: s = Spectrum(y)
  
  In [5]: # display spectrum \
     ...: s
  Out[5]:
  [ 0.03935584 -0.05252603  0.00796074  0.0597675  -0.07945138  0.0079739
    0.11489588 -0.17056501  0.00798048  0.82957457  0.82957457  0.00798048
   -0.17056501  0.11489588  0.0079739  -0.07945138  0.0597675   0.00796074
   -0.05252603  0.03935584]
  [  1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.  14.  15.
    16.  17.  18.  19.  20.] pix
  
  In [9]: # where no units are given, they are implied. \
     ...: s[2]
  Out[9]: <Quantity -0.05252602557386098>
  
  In [10]: # that was not the second element of the spectrum, but the signal \
     ....: # at the location `2 pix`. Accessing the spectrum where it is not \
     ....: # defined returns a linear approximation to it between the two \
     ....: # pixels that surround it. \
     ....: s[2.5]
  Out[10]: <Quantity -0.02228264076183217>
  
  In [11]: # units are an acceptable access method, so long as they can be \
     ....: # converted (e.g., u.nm -> u.Angstrom) \
     ....: s[2.5 * u.pixel]
  Out[11]: <Quantity -0.02228264076183217>
  
  In [12]: # Slicing the spectrum works in two ways. If no `step` is given \
     ....: # than the `start` and `stop` act as a domain for which we \
     ....: # want to truncate the spectrum outside of. \
     ....: s[2:4]
  Out[12]:
  [-0.05252603  0.00796074  0.0597675 ]
  [ 2.  3.  4.] pix
  
  In [13]: s[2.5:4.5]
  Out[13]:
  [ 0.00796074  0.0597675 ]
  [ 3.  4.] pix
  
  In [14]: s[:4]
  Out[14]:
  [ 0.03935584 -0.05252603  0.00796074  0.0597675 ]
  [ 1.  2.  3.  4.] pix
  
  In [15]: s[15:]
  Out[15]:
  [ 0.0079739  -0.07945138  0.0597675   0.00796074 -0.05252603  0.03935584]
  [ 15.  16.  17.  18.  19.  20.] pix
  
  In [16]: # With a `step` size the behavior is entirely different. \
     ....: # We are asking instead to `resample` the spectrum. The  \
     ....: # `step` value acts as a desired resolution. As with the \
     ....: # accessor methods, the gaps are filled with linear      \
     ....: # approximations \
     ....: s[1:3:0.5]
  Out[16]:
  [ 0.03935584 -0.00658509 -0.05252603 -0.02228264  0.00796074]
  [ 1.   1.5  2.   2.5  3. ] pix
  
  In [17]: s = s[:4] \
     ....: s
  Out[17]:
  [ 0.03935584 -0.05252603  0.00796074  0.0597675 ]
  [ 1.  2.  3.  4.] pix
  
  In [18]: # Without a `start` and a `stop` we just resample. \
     ....: s[::0.5]
  Out[18]:
  [ 0.03935584 -0.00658509 -0.05252603 -0.02228264  0.00796074  0.03386412
    0.0597675 ]
  [ 1.   1.5  2.   2.5  3.   3.5  4. ] pix
  
  In [19]: s = Spectrum(y) \
     ....: s
  Out[19]:
  [ 0.03935584 -0.05252603  0.00796074  0.0597675  -0.07945138  0.0079739
    0.11489588 -0.17056501  0.00798048  0.82957457  0.82957457  0.00798048
   -0.17056501  0.11489588  0.0079739  -0.07945138  0.0597675   0.00796074
   -0.05252603  0.03935584]
  [  1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.  14.  15.
    16.  17.  18.  19.  20.] pix
  
  In [20]: # The boundaries take precident however, and not every resolution \
     ....: # makes physical sense with the requested boundaries. When no     \
     ....: # edges are specified, they default to the current boundaries.    \
     ....: # There are only 19 pixels, so we won't get what you might think  \
     ....: # the expected behavior is. Here, 5 doesn't go evenly into the    \
     ....: # existing domain, so we simply choose the closest thing.
     ....: s[::5]
  Out[20]:
  [ 0.03935584  0.01974225  0.01974225  0.03935584]
  [  1.           7.33333333  13.66666667  20.        ] pix
  

  
  Member functions:

  • resample ( *args, kind = 'linear' ):

    If given a single argument, it is taken to be a Spectrum object, and self is resampled onto the pixel space of the other spectrum. Otherwise, three arguments are expected. The first and second argument should define the lower and upper wavelength value of a domain, respectively. The third argument should be the number of elements (pixels) for the new domain. Think numpy.linspace(). kind is passed to scipy...interp1d.

    spectrumA.resample(spectrumB, kind = 'cubic')
    
    spectrumA.resample( 585 * u.nm, 5950 * u.Angstrom, 1e4 + 1 )

  • insert ( other, kind = 'linear'):

    Given a Spectrum, other, contained within the wavelength domain of self, replace all pixels in the overlapping region with that of an interpolation built on other. kind is passed to interp1d.

    spectrumA.insert(spectrumB, kind = 'quadratic')

  • copy ():

    Essentially a wrapper to deepcopy(). To say SpectrumA = SpectrumB implies that SpectrumA is SpectrumB. If you want to create a new spectrum equal to another, say SpectrumA = SpectrumB.copy()

Fits

Manipulate FITS files. Import data into Spectrum objects. Filter results by right ascension and declination. Grab header elements. Search for attributes of the data such as distance, spectral type, etc.

• Find (toplevel = './', pattern = '*.fits'):

  Search for file paths below toplevel fitting pattern. Returns a list of string values.

• RFind (toplevel = './', pattern = '*.fits'):

  Recursively search for file paths below toplevel fitting pattern. Returns a list of string values.

• GetData ( *files, **kwargs):

  Import data from FITS files. Returns a list of Spectrum objects.

  Options	Defaults	Descriptions
  verbose	True	display messages, progress
  toplevel	''	request import from directory toplevel
  pattern	'*.fits'	pattern matching with toplevel
  recursive	False	search recursively below toplevel
  wavecal	True	fit wavelength vector to data
  crpix1	'crpix1'	reference pixel header keyword
  crval1	'crval1'	value at reference pixel
  cdelt1	'cdelt1'	resolution (delta lambda)
  xunits	'Angstrom'	wavelength units (astropy.units)
  yunits	'ergs cm-2 s-1'	units of the data

• Header ( filename, keyword = None, **kwargs):

  Retrieve keyword from FITS header in file filename. Return type depends on what is returned. If no keyword is given, the entire header object is returned.

• Search ( *files, **kwargs):

  Extract object names from Fits files and use Simbad module to resolve the attribute (a required keyword argument) from the SIMBAD astronomical database. Currently available attributes are 'Position', 'Distance', 'Sptype', and 'IDList'. Returns a list of results (type depends on the values).

  Options	Defaults	Descriptions
  verbose	True	display messages, progress
  toplevel	None	search under toplevel directory
  pattern	'*.fits'	for files under toplevel
  recursive	False	search recusively under toplevel
  attribute	None	attribute to search for (no default)

• PositionSort ( center, radius, *files, **kwargs ):

  Return a list of files from files that lie in a radius (in decimal degrees) from center, based on the ra (right ascension) and dec (declination).

  Options	Defaults	Descriptions
  ra	'pos1'	header element for right ascension
  dec	'pos2'	header element for declination
  obj	'object'	header element for object id
  raconvert	True	convert decimal hours to decimal degrees
  verbose	True	display messages, progress
  toplevel	None	toplevel directory to look for files
  recursive	False	search below toplevel recursively
  pattern	'*.fits'	glob pattern for file search
  useSimbad	False	use Simbad instead of header elements

Simbad

This module allows the user to query the SIMBAD astronomical database from inside Python or shell commands/scripts. It's four current major functions Position, Distance, Sptype, and IDList return real variables with appropriate types ready for use.

As a shell script:

$ Simbad.py

 usage: Simbad.py @Attribute <identifier> [**kwargs]

 The 'Attribute' points to a function within this module and indicates
 what is to be run. Execute 'Simbad.py @Attribute help' for usage details of
 a specific function. Currently available attributes are: `Position`,
 `Distance`, `Sptype` and `IDList`.

 The identifier names can be anything recognized by SIMBAD (e.g., Regulus,
 "alpha leo", "HD 121475", "del cyg", etc ...) if the name is two parts make
 sure to use quotation to enclose it.

 The **kwargs is the conventional reference to Python keyword arguments.
 These should be specific to the 'Attribute' being pointed to.

• Position ( identifier, **kwargs ):

  Return the right ascension and declination in decimal degrees of identifier as a pair.

  Options	Defaults	Descriptions
  parse	True	parse return file from SIMBAD
  full	False	return more detailed information

  
  Example:

  ra, dec = Simbad.Position('Sirius')

• Distance ( identifier, **kwargs ):

  Return the distance in parsecs to identifier.

  Options	Defaults	Descriptions
  parse	True	parse return file from SIMBAD
  full	False	return more detailed information

  
  Example:

  distance = Simbad.Distance('rigel kent')

• Sptype ( identifier, **kwargs ):

  Return the spectral type of identifier as resolved by SIMBAD.

  Options	Defaults	Descriptions
  parse	True	parse return file from SIMBAD
  full	False	return more detailed information

  
  Example:

  # returns 'B8IVn' (HD 87901 is Regulus)
  sptype = Simbad.Sptype('HD 87901')

• IDList ( identifier, **kwargs ):

  Return a list of alternate IDs for identifier.

  Options	Defaults	Descriptions
  parse	True	parse return file from SIMBAD
  full	False	return more detailed information

  
  Example:

  other_names = Simbad.IDList('proxima centauri')

Correlate

Correlation functions for astronomical data.

• XCorr ( spectrumA, spectrumB, **kwargs ):

  The function returns an integer value representing the best shift within a lag based on the computed RMS of each configuration.

  Options	Defaults	Descriptions
  lag	25	pixel range to shift over

Telluric

Removal of atmospheric absorption lines in spectra.

• Correct ( spectrum, *calibration, **kwargs ):

  Perform a telluric correction on spectrum with one or more calibration spectra. If more than one calibration spectrum is provided, the one with the best fit after performing both a horizontal cross correlation and a vertical amplitude fit is used. The spectrum and all the calibration spectra must have the same number of pixels (elements). If a horizontal shift in the calibration spectra is appropriate, only the corresponding range of the spectrum is divided out!

  Notice Your spectra must be continuum normalized for this to work!

  Options	Defaults	Descriptions
  lag	25	pixel range to shift over
  range	(0.5, 2.0, 151)	numpy.linspace for amplitude fitting

  

  Figure 1: The above figure is an example of applying the Telluric.Correct() algorithm to a spectrum. In this case, six spectra of Regulus from the Elodie archive were used as calibration spectra.

Velocity

Radial velocity corrections for 1D spectra.

• HelioCorrect ( observatory, *spectra, **kwargs ):

  Perform heliocentric velocity corrects on spectra based on observatory parameters (longitude, latitude, altitude) and the member attributes, ra (right ascension), dec (declination), and jd (julian date) from the spectra. These should all have units.

  Options	Defaults	Descriptions
  verbose	False	display messages, progress

• BaryCorrect ( observatory, *spectra, **kwargs ):

  Perform barycentric velocity corrects on spectra based on observatory parameters (longitude, latitude, altitude) and the member attributes, ra (right ascension), dec (declination), and jd (julian date) from the spectra. These should all have units.

  Options	Defaults	Descriptions
  verbose	False	display messages, progress

• IrafInput ( *files, **kwargs ):

  Build an input file for IRAF's rvcorrect task.

  files should be a list of FITS file names to build the output table for. The user can optionally specify a toplevel directory to search for files under. If outfile is given, write results to the file.

  Options	Defaults	Descriptions
  toplevel	None	search toplevel directory for files
  pattern	'*.fits'	pattern matching under toplevel
  recursive	False	search recusively under toplevel
  outfile	None	write lines to file named outfile

Observatory

Define observatory parameter similar to the IRAF task. All observatories should follow the following pattern. The user can add as many as they like to this module. I welcome suggestions.

class OHP(Observatory):
    """
    The Observatoire de Haute-Provence, France.
    """
    def __init__(self):
        self.name       = 'Observatoire de Haute-Provence'
        self.longitude  = 356.28667  * u.degree # West
        self.latitude   = 43.9308334 * u.degree # North
        self.altitude   = 650        * u.meter
        self.timezone   = 1          * u.hourangle
        self.resolution = 42000

There are currently 69 defined observatories:

Plot

Convenient wrappers to matplotlib for plotting spectra. A SPlot simply creates a handle to remember figure attributes, to quickly go from looking at one spectra to another. One can also overlay spectra.

• class SPlot ( spectrum, **kwargs ):

  Spectrum Plot - Create figure of spectrum.

  Options	Defaults	Descriptions
  marker	'b-'	marker for data
  label	'spectrum'	name of object
  usetex	False	render with pdflatex

  
  The following member functions call pyplot equivalent:
  xlim, ylim, xlabel, ylabel, title, legend, text, grid, close.

  Here, when these function are called, the arguments are passed to matplotlib; however, these calls are remembered. So when you go to draw the figure again, you are back where you left off.

  • draw( ):

    Rebuild and render the figure.

  • refresh( ):

    Re-render (refresh) the figure, without clearing the axis.

  • txtclear( ):

    Clear all the calls to text( ) from the figure.

  • xoffset( value ):

    Switch either on or off (value = True | False) the horizontal offset.

  • yoffset( value ):

    Switch either on or off (value = True | False) the vertical offset.

  • overlay( *splots ):

    Given one or more splots, overlay the figures.

  • markers( *args ):

    Reassign the values for the markers in the figure. The number of arguments must equal the number of spectra in the figure. This starts out as one, but will increase for ever call to SPlot.overlay().

  • restore( ):

    Restore the figure to only the original spectrum.

  • save( filename ):

    Save the figure to a file called filename. The file format is derived from the extention of filename.

• Iterate( *splots, **kwargs ):

  Iterate thru splots to inspect data, the user marks spectra of interest. The function returns a list of keepers.

  Options	Defaults	Descriptions
  keep	'name'	alternative is 'plot'

  
  Example:

  from slipy import Fits, Plot
  
  fpath = '?' # toplevel directory name where your FITS files are
  files = Fits.Find(fpath)
  
  spectra = Fits.GetData( *files )
  
  figure = [
  
      Plot.SPlot( spectrum, label=Fits.Header(fname, 'object') )
      for spectrum, fname in zip(spectra, files)
  ]
  
  keepers = Plot.Iterate( *figure )
  # enter either 'y', 'n', or 'x' as prompted by the terminal

Profile

Profile fitting tasks for spectra.

• Select ( splot ):

  Select points from the splot. This should be of type SPlot (or it can optionally be a Spectrum type, for which a SPlot will be created). The splot will be rendered and the user clicks on the figure. When finished, return to the terminal prompt. A dictionary is returned with two entries, wave and data, representing the x-y locations selected by the user. This can always be retrieved later by accessing the module member Profile.selected.

  While the user makes selections, temporary markers appear on the figure indicating the data point that was just selected. If a mark does not appear, try moving the curser slightly and trying again. Even if the line goes through that point, there might not actually be data there.

• AutoFit ( splot, function = InvertedLorentzian, params = None)

  Given a splot of type Plot.SPlot, the user selects four points on the spectrum and a parameterized function is fit (an inverted Lorentzian by default). Optionally, splot can be of type spectrum and a basic SPlot will be created for you. If the user gives an alternative function, params (parameters) must be provided. params is to be the first guess, p0 given to scipy...curve_fit; the user can provide them expicitely, or in the form of functions with the templates f(x, y) where x and y are the wave and data arrays (respectively) extracted between the two inner points selected by the user.

  InvertedLorentian is defined in SLiPy.Algorithms.Functions. The user does not need to provide params for the default behavior.

  Example:

  # In this example I use an alternative function simply as an illustration.
  # Here `spectrum` is a Spectrum object that has been imported and calibrated ...
  
  from slipy import Spectrum, Plot, Profile
  from astropy import units as u
  
  
  # create a SPlot figure
  fig = Plot.SPlot(spectrum, label='HD332329', marker='k-')
  fig.xlabel('Wavelength (Angstrom)', labelpad=20)
  fig.ylabel('Normalized Flux', labelpad=20)
  fig.xlim(5885, 5905)
  fig.legend(frameon=False)
  
  # Now we need to define some parameter functions to pass to Profile.Fit()
  # with our user function
  from slipy.Algorithms.Functions import InvertedGaussian
  
  # best guess for amplitude of gaussian
  def A(x, y):
      return 1 - y.min().value
  
  # best guess for mean of gaussian
  def mu(x, y):
      return x[ y.argmin() ].value
  
  # best guess for standard deviation of gaussian
  def sigma(x, y):
      return y.std().value
  
  # call the Profile.Fit() function with our user defined parameterization
  line = Profile.AutoFit(fig, function=InvertedGaussian, params=[A, mu, sigma])
  
  # Please select four points identifying the spectral line.
  # Outer points mark the domain of the line.
  # Inner points mark the sample of the line to fit.
  # Press <Return> after making your selections ...
  
  # now `line` is a Spectrum object generated by evaluating the
  # `InvertedGaussian` function on the larger domain selected by the
  # user with coefficients optimized using Profile.AutoFit().
  
  # save the figure ...
  fig.tight_layout()
  fig.xoffset(False)
  fig.refresh()
  fig.save('HD332329.png')

  

  Figure 2: The above figure was generated by running the above code snippet.

• Extract ( splot, kernel = Gaussian, **kwargs):

  Select locations in the splot figure, expected to be of type SPlot. Exactly four points should be selected. These are used to extract a line profile from the spectrum plotted in the splot figure. The inner section is used for the line, and the outer intervals are used to model the continuum; these, respectively, are both returned as Spectrum objects. The gap is jumped using 1D interpolation (scipy...interp1d). In order to get a smooth continuum and error estimates, this function uses non-parametric kernel regression (a.k.a. kernel smoothing) to fit a curve through the data marked as continuum. Be default, the kernel function is the Gaussian. The user can actually use a different, user defined kernel function, but it's template must match that of SLiPy.Algorithms.Functions.Gaussian. See the SLiPy.Algorithms.KernelFit module to better understand how this happens. The bandwidth is actually the standard deviation in the Gaussian. It is a length scale that defines how influencial data is at a particular distance. The default bandwidth will almost definitely be too large (resulting in a largely flat result at the average of the data points). As the bandwidth decreases to smaller than the resolution of the data, you will essentially being performing linear interpolation (no smoothing). You want to choose a length that is larger than the small scale variation in the noise, but smaller than the larger scale variation in the overall continuum level.

  The rms can optionally be computed between the continuum data and the curve fit to it. This rms value is used to scale the error profile that is returned for the line extracted. Explicitely, the error for a line extraction is error_spectrum = continuum_rms * I_0 / I where I is the line data and I_0 is the interpolated continuum over the gap.

  From the documentation of scipy.interpolate...interp1d: kind specifies the kind of interpolation as a string ('linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic') where 'slinear', 'quadratic' and 'cubic' refer to a spline interpolation of first, second, or third order respectively; or as an integer specifying the order of the spline interpolator to use.

  Options	Defaults	Descriptions
  kind	'cubic'	given to scipy...interp1d for continuum
  bandwidth	0.1 * u.nm	for kernel, user should provide this!
  rms	False	return an error estimate for the line

  
  Example:

  # drawing from the previous example, but using HD200723 instead.
  
  line, continuum, rms = Profile.Extract(fig, bandwidth=u.Angstrom/15, rms=True)
  
  # Please select four points identifying the spectral line.
  # Outer intervals sample the continuum.
  # Center interval contains the line.
  # Press <Return> after making your selections ...

  

  Figure 3: The above figure was generated by running the above code snippet.

• MultiFit ( splot, function = 'Lorentzian', observatory = None, resolution = None, **kwargs):

  The MultiFit routine takes a splot figure (type SPlot) and allows the user to interactively fit line profiles. splot may optionally be of type Spectrum, in which case a SPlot figure will be created for you. This function creates a FittingGUI object (not documented here) which uses the Profile.Extract() routine first (kwargs as passed this function). As in the Extract routine, the user will select four points that mark the boundaries of the line (blended or otherwise) and some surrounding continuum to sample. The KernelFit1D routine is applied with the user specified bandwidth to smooth the noise out of the continuum and interpolate, of type kind, across the line. After this, the user is asked to further select points marking the peaks of the line(s). The number of selection points indicates the number of lines to be fit. If you wish to deblend two or more lines, select all suspected locations. These are not only used to determine the number of lines to fit but to make initial guesses at the parameters for each line.

  After these selections, a slider for each parameter appears along with radio buttons for each line. The user can interactively adjust the parameter(s) for a given line by selecting it (the radio buttons) and dragging a slider. Each line is represented by a black, dashed line in the plot. The currently selected line is bold. The super-position (i.e., blended) lines are plotted with a solid green line.

  Each slider is labeled on the left side with the name of the parameter it controls. At the right of each slider is the current value of that parameter. The radio buttons are labeled "L1", "L2", etc. for each line.

  This routine returns a list of Spectrum objects. The length of the list is equal to the number of lines that were fit. This routine functions as both a single line fitting tool as well as a deblending tool.

  Example:

  # drawing from the above examples
  
  L1, L2, L3, L4 = Profile.MultiFit(fig, function = 'Gaussian', bandwidth = u.Angstrom / 8)
  
  #  We need to extract the lines from the continuum before we begin the fitting process.
  # Please select four points identifying the spectral line.
  # Outer intervals sample the continuum.
  # Center interval contains the line.
  # Press <Return> after making your selections ...
  
  # Now select the peaks of each line to be fit.
  # Initial guesses will be made for each line markered.
  # Press <Return> after making your selections ...

  

  Figure 4: Mid-session, annotated screen-shot of the above code-snippet/routine.

Montage

Montage is a very powerful suite of C code for creating image mosaics. This module is a wrapper to automate not only the process for small projects, but to segment large fields into a grid and mosaic each site before combining them into a master mosaic.

At the outset of the development of this module, the author was unaware of the currently available montage_wrapper. This module will no longer be developed. Nevertheless, it remains a high level manager for creating mosaics.

See the example in Figure 5.

• SolveGrid ( sides, grid ):

  Helper function for the Field and SubField classes. Both sides and grid need to be array-like and of length two. sides is the side length of the field in decimal degrees in right ascension and declination respectively. grid specifies the subdivision along these axis (e.g., (2,2) says 2x2).

  The user should mindful of their choices. If the side lengths cannot be subdivided into well-behaved (rational) segments, higher decimal places will be lost in the SubField.ArchiveList() task resulting in small gaps in the mosaic.

• Mosaic ( resolution, *folders, **kwargs ):

  Conduct standard build procedures for all folders, similar to the m101 example. resolution is the number of pixels per degree for the output image. Note: folders should be absolute paths. Further, below each of these directories, there should already exist the standard folder structure

  folder/
    |--images/
    |    |-- <location of raw FITS images>
    |
    |--projected/
    |--differences/
    |--corrected/
    |--final/
  

  
  The mosaic will be deposited at final/mosaic.fits.

  Options	Defaults	Descriptions
  verbose	True	display messages, progress
  bkmodel	True	model and correct for background effects

• class SubField ( center, sides, grid, **kwargs ):

  Create a grid of sites each of which will be mosaiced separately and then combined. Each of center, sides, and grid should be array-like and of length two. center should be the very center location for the mosaic in right ascension and declination (both in decimal degrees), respectively. sides needs to give the side lengths of the desired mosaic in decimal degrees (width-RA, height-DEC). grid should be the grid division for the field (e.g., (2, 2) means 2x2 grid).

  There will be a directory created for each site and also another master directory. The final resulting mosaic will be deposited at final/mosaic.fits here.

  Options	Defaults	Descriptions
  verbose	True	display messages, progress
  survey	'DSS'	DSS, SDSS, 2MASS
  band	'DSS2B'	filter for survey, see bands dict
  pad	0.0	amount to add (degrees) around sites

  
  The available filter band for each survey are as follows

  Survey	Bands
  '2MASS'	'J', 'H', 'K'
  'SDSS'	'U', 'G', 'R', 'I', 'Z'
  'DSS'	'DSS1B', 'DSS1R', 'DSS2B', 'DSS2R', 'DSS2IR', 'Quick-V'

  
  The user should execute the following available methods in this order:

  • ArchiveList ( **kwargs ):

    Run the mArchiveList command on the site grid. The only keyword argument is verbose which defaults to True.

  • ArchiveExec ( **kwargs ):

    Run mArchiveExec on each site in the SubField. The only keyword argument is verbose which defaults to True.

  • Build ( resolution, **kwargs):

    Run the build process for the sites in this SubField. See the Montage.Mosaic() function documentation.

  • Collect ( **kwargs ):

    Collect the mosaics from all site locations into a master images folder. The only keyword argument is verbose which defaults to True.

  • Merge ( resolution, **kwargs ):

    Merge all site mosaics into a single master SubField mosaic. We are now calling Montage.Mosaic() on each site.

  
  Example:

  from slipy import Montage, Simbad
  
  # initialize the mosaic with a location, size, grid
  mosaic = Montage.SubField(
  
      Simbad.Position('Pleiades'), # center of the Pleiades
      (4, 4),                      # side lengths of mosaic
      (2, 2),                      # grid pattern creates 2x2 degree `sites`
      band = 'DSS2B'               # Blue filter
  )
  
  # run mArchiveList to get the raw image tables
  mosaic.ArchiveList()
  
  # download the images at all `sites`
  mosaic.ArchiveExec()
  
  # run Montage.Mosaic() at each site, create a 4MP image,
  # Notice: this creates > 12 GB on disk!
  mosaic.Build(3/2000, bkmodel=False)
  
  # copy `site` mosaics to master image directory
  mosaic.Collect()
  
  # run Montage.Mosaic() on `site` mosaics
  mosaic.Merge(3/2000, bkmodel=False)
  
  # If you have APLpy
  import aplpy
  fig = aplpy.FITSFigure('master/final/mosaic.fits')
  fig.show_grayscale()
  fig.set_frame_color('gray')
  fig.set_frame_linewidth(0.5)
  fig.set_nan_color('black')
  fig.set_tick_labels_xformat('hh:mm')
  fig.set_tick_labels_yformat('dd:mm')
  
  from matplotlib import pyplot as plt
  plt.rc('text', usetex=True)
  plt.rc('font', family='serif')
  plt.draw()
  
  fig.save('Pleiades.png')

  

  Figure 5: Pleiades.png from the above code snippet.

• class Field (center, sides, grid, subgrid, **kwargs ):

  Large image mosaic manager for Montage. This class (in terms of its usage) is the same as the SubField class, except that here we managing the subfields. So all the member functions are the same name and purpose, but instead act to call that same function on each subfield. Here, all the constructor arguments are as before, with the additions of subgrid which is also to be array-like of length two. grid will be the first level division to find the centers and side lengths of all the subfields and subgrid will be the further sub-division passed down to the daughter subfields.

  Options	Defaults	Descriptions
  verbose	True	display messages, progress
  survey	'DSS'	DSS, SDSS, 2MASS
  band	'DSS2B'	filter for survey, see bands dict
  pad	0.0	amount to add (degrees) around sites

  
  The available filter band for each survey are as follows

  Survey	Bands
  '2MASS'	'J', 'H', 'K'
  'SDSS'	'U', 'G', 'R', 'I', 'Z'
  'DSS'	'DSS1B', 'DSS1R', 'DSS2B', 'DSS2R', 'DSS2IR', 'Quick-V'

  
  All the member functions are the same name as in SubField, but now with the addition of a final step:

  • Finalize ( resolution, **kwargs ):

    Collect all SubField/master mosaics into a single folder and run Mosaic() on them for a single final image.

Elodie

Methods for data retrieval from the Elodie Archive.

• class Archive ( **kwargs ):

  Import and parse ascii catalog of Elodie archive files. The complete archive is stored in the member dictionary, data. It's organized by unique target names. Each target has a list of pairs consisting of the name of the file and the signal to noise for that spectrum. The reduced archive by default contains only HD, BD, HR, GC, and GJ objects, choosing the file pertaining to the spectra with the highest signal-to-noise ratio available.

  Options	Defaults	Descriptions
  infile	archives/elodie.csv	path to input file
  catalogs	['HD','BD','HR','GC','GJ']	catalogs to keep

  
  Example:

  from slipy.Data import Elodie
  
  archive = Elodie.Archive()
  
  'HD187642' in archive.files # returns True (Altair)
  'HD045348' in archive.files # returns False (Canopus)

• Script ( filename, pipeline = '' ):

  Construct url script for Elodie archive given filename and optionally pipeline instructions (e.g., '&z=wrs|fca[1,nor]').

• Download ( *files, **kwargs ):

  Download files from Elodie archive via url scripts. The spectra can be further reduced via Elodie's pipeline with the following options.

  Options	Defaults	Descriptions
  verbose	True	display messages, progress
  resample	(min, max, res)	resample spectra (no default)
  normalize	True	continuum normalization
  outpath	'./'	directory for downloaded files
  names	[]	alternative output names for files

  
  Example:

  # continuing from the previous example ...
  
  # all files in the archive for Altair (file name is first element in pair)
  files = [ entry[0] for entry in archive.data['HD187642'] ]
  
  # download files to current directory, resample spectrum to wavelengths
  # between 5850 and 5950 with a resolution of 0.01 Angstroms per pixel
  Elodie.Download( *files, resample=(5850, 5950, 0.01) )

Atomic

Convenient access to a large set of published atomic data for absorption lines:

``Atomic data for resonance absorption lines. III. Wavelengths longward of the Lyman limit for the elements Hydrogen to Gallium''
Author: Donald C. Morton (2003)
Online: http://iopscience.iop.org/0067-0049/149/1/205/fulltext/

• class IonManager ():

  Managing class for the atomic data (Morton 2003). See SLiPy.Data.Archives.AtomicData.

  • __call__ ( key, **kwargs ):

    Retrieve data from the Archives.AtomicData table. If the key is a string type it is expected to be the name of an ion (e.g., 'C III'). If the key is a number it is expected to be a wavelength value (if not with units Angstroms are implied). The default is vacuum wavelength, but air can be specified with the keyword argument wavelength='Air'.

    If the key was the name of an ion, all the lines for that ion are returned. If the key was a wavelength, the closest line in the table to that wavelength is returned. You can request a wavelength range by giving the key as a tuple of two wavelengths specifying the range.

    The results default to the f-value (a.k.a. the oscillator strength) but can be changed with the keyword argument lookup. Options include, air, vacuum, ion, elow, logwf, and fvalue.

    The return is either a single pair or a list of pairs: the first element of each pair is always a wavelength value (in air if wavelength='air' or in vacuum otherwise), the second being the entries requested. The wavelength type is always that used for the look-up. That is, Vacuum by default, but if wavelength='Air' is given, the returns will be in air wavelengths. Be aware that None might be returned if there is not Air wavelength for the line. Further, all results will be returned as None where no data is available.

    Options	Defaults	Alternatives
    wavelength	'vacuum'	'air'
    lookup	'fvalue'	'air', 'vacuum', 'ion', 'elow', 'logwf'

The IonManager can be imported via the member instance IonSearch.

Examples:

Import the data set:

from slipy.Data.Atomic import IonSearch
# equivalent to `from slipy.Data.Atomic import IonManager; IonSearch = IonManager()`

The member .data contains the entire table from Morton 2003. We can access information of interest directly however by calling the Ions object.

IonSearch('C III')

[(<Quantity 977.0201 Angstrom>, 0.7570010842747638),
 (<Quantity 1908.734 Angstrom>, 1.6875419997146983e-07)]

The second item in each of those pairs was the oscillator strength, or fvalue, for the lines. The wavelength given was in vacuum. We can request the same lines in air:

IonSearch('C III', wavelength='air')

[(None, 0.7570010842747638), (None, 1.6875419997146983e-07)]

The result is None because those values don't exist in the data set. But they are indeed the same lines.

We can ask for a wavelength range, and change what we are looking up:

IonSearch( (5885, 5900), wavelength='air', lookup='ion')

[(<Quantity 5889.951 Angstrom>, 'Na I'),
 (<Quantity 5894.092 Angstrom>, 'Ti I'),
 (<Quantity 5895.924 Angstrom>, 'Na I')]

Units work as well:

from astropy import units as u
IonSearch( (588.5 * u.nm, 590.0 * u.nm), wavelength='air', lookup='ion')

[(<Quantity 5889.951 Angstrom>, 'Na I'),
 (<Quantity 5894.092 Angstrom>, 'Ti I'),
 (<Quantity 5895.924 Angstrom>, 'Na I')]

• class Ion ( name = None, wavelength = None, fvalue = None, A = None, **kwargs):

  An object for declaring atomic ions.

  An Ion should have a name, wavelength (air or vacuum), osciallator strength, and a transition probability (Einstein coefficient).

  Much of this data is available from the ..Data.Archives.AtomicData module searchable with the ..Data.Atomic.IonManager class. In the future, we hope to implement an internal database of transition probabilities for each ion in the AtomicData module from the Morton 2003 publication. Currently, the user must provide this data.

  The NIST Atomic Spectra Database Lines Data is a good source for atomic data values: http://physics.nist.gov/PhysRefData/ASD/lines_form.html

  If no arguments are given the state remains uninitialized. The name (e.g., 'C III') is used to connect with the data in the ..Data.Archives.AtomicData module via the IonManager class. The wavelength need not necessarily be the exact value of the line; the line closest to that given for the named ion is used. This is by default the wavelength in vacuum, but can be the wavelength in air if the keyword argument medium='air' is given. The created member attribute wavelength* and fvalue are automatically assigned based on this procedure, but can be explicitly assigned if the fvalue is provided directly. The transition probability (Einstein coefficient) A is simply attached as A. If no units are given for A, per second is assigned.

Example:

from slipy.Data import Atomic
D2 = Atomic.Ion('Na I', 589.1 * u.nm, A = 6.16e7 / u.s)
D2

Ion:        Na I
Wavelength: 589.1583300000001 nm
fvalue:     0.6408671009122726
A:          61600000.0 1 / s