Converting Between Decimal Degrees and Hours, Minutes, Seconds

Here's a quick Python snippet I wrote to convert right ascension in decimal degrees to hours, minutes, seconds and declination to (+/-)degrees, minutes, seconds.

def deg2HMS(ra='', dec='', round=False):
  RA, DEC, rs, ds = '', '', '', ''
  if dec:
    if str(dec)[0] == '-':
      ds, dec = '-', abs(dec)
    deg = int(dec)
    decM = abs(int((dec-deg)*60))
    if round:
      decS = int((abs((dec-deg)*60)-decM)*60)
      decS = (abs((dec-deg)*60)-decM)*60
    DEC = '{0}{1} {2} {3}'.format(ds, deg, decM, decS)
  if ra:
    if str(ra)[0] == '-':
      rs, ra = '-', abs(ra)
    raH = int(ra/15)
    raM = int(((ra/15)-raH)*60)
    if round:
      raS = int(((((ra/15)-raH)*60)-raM)*60)
      raS = ((((ra/15)-raH)*60)-raM)*60
    RA = '{0}{1} {2} {3}'.format(rs, raH, raM, raS)
  if ra and dec:
    return (RA, DEC)
    return RA or DEC

For example:
In [1]: f.deg2HMS(ra=66.918277)
Out[1]: '4 27 40.386'

Or even:
In [2]: f.deg2HMS(dec=24.622590)
Out[2]: '+24 37 21.324'

Or if you want to round the seconds, just do:
In [3]: f.deg2HMS(dec=24.622590,round=True)
Out[3]: '+24 37 21'

And to convert hours, minutes and seconds into decimal degrees, we have:

def HMS2deg(ra='', dec=''):
  RA, DEC, rs, ds = '', '', 1, 1
  if dec:
    D, M, S = [float(i) for i in dec.split()]
    if str(D)[0] == '-':
      ds, D = -1, abs(D)
    deg = D + (M/60) + (S/3600)
    DEC = '{0}'.format(deg*ds)
  if ra:
    H, M, S = [float(i) for i in ra.split()]
    if str(H)[0] == '-':
      rs, H = -1, abs(H)
    deg = (H*15) + (M/4) + (S/240)
    RA = '{0}'.format(deg*rs)
  if ra and dec:
    return (RA, DEC)
    return RA or DEC

So we can get back our decimal degrees with:

In [4]: f.HMS2deg(ra='4 27 40.386', dec='+24 37 21.324')
Out[4]: (66.918, 24.622)

How to Make Animated GIFs of Plots

So you're swimming in countless plots of your data over some changing parameter. Sure they're nice to look at, but are they animated? Didn't think so.

Here's how to create an animated .gif image of those Python plots using Photoshop.

Step 1: Generate the plots

I've found that a few lines of Python to programmatically draw and save your plots to a folder eliminates a lot of editing and tweaking later on.

For this tutorial, I'll use my synthetic photometry code to generate color-parameter plots across surface gravities of 3.0dex to 6.0dex.

First I created a folder on my desktop to dump the plots called RI_teff.

Then in the Python interpreter it looks like:

In [1]: grav = [round(i*0.1,1) for i in range(30,61)]
In [2]: import syn_phot as s
In [3]: for i in g:

Now I can create the animated .gif in Photoshop.

Step 2: Pull plots into Photoshop as layers

Open Photoshop and click File > Scripts > Load Files into Stack...

Select the folder on your Desktop that has all the plots and click ok.

Photoshop will open each image as a layer in a new project window.

Step 3: Create frames from each layer

Next click Window > Timeline to show the Timeline across the bottom of the program.

In the top-right corner of your Timeline, you'll see a little button that has all the Timeline options. Click it and select Make Frames From Layers. Here's what it looks like:

This will populate your Timeline with one frame for each image layer.

Click the Timeline options button again and click Reverse Frames if necessary. Otherwise, you can drag and drop the frames into the desired order.

Step 4: Timing is everything

Next we need to set the timing of each frame. Select the first frame from the Timeline then hold down the Shift button and select the last frame to select them all.

Next click on any frame where it says 0 sec. with a down arrow. Then select the display time for each frame in the animation.

I typically set the frames to be 0.2 or 0.5 seconds each, depending on the number of total frames. Then I set the last frame to be 2 seconds so it's as if the image pauses when it finishes before starting the animation over.

Step 5: Save it!

Finally, click File > Save for Web... and make sure you have GIF filetype selected. Click Save and you're done! Here's the result:

Editing PYTHONPATH (or "Where's my module?!")

Python not finding your modules with import MyModule for some reason? It's probably your PYTHONPATH. Here's how you edit it so that Python sees all your modules.

A Word About Directories

Firstly, if you are the kind of person who keeps files scattered about your computer, dumps everything into the root directory, or is afraid to use those nice little blue folders, then perhaps programming and computers in general are not for you.

Logical and neat directory structure will make your own, your computer's and your collaborators' lives much easier.

My recommendation: Create a master code directory (called "Modules" or something) in your Documents folder. This will be the new home of all your future Python projects.

Now every time you create a .py file, it should go into a project folder inside your Modules directory, i.e. /Documents/Modules/NewProject/ Note that you should have no actual modules inside your Modules directory! Keep those puppies in their warm, snuggly project subdirectories.

This way you can also initialize that project folder (i.e. /NewProject) as a Git repository and just push and pull the contents to keep it up-to-date!


Python won't just search your computer for the file you're trying to import. You have to tell it explicitly each time where to get it. The PYTHONPATH is a list of directories for your computer to check whenever you type import MyModule into the interpreter.

To add a path, launch ipython and type:

import sys
print sys.path

Note: You only have to update your PYTHONPATH once, not every time you use Python!

So now your path is updated but this is only the path to your master Python folder.

In order to have Python see the modules inside each subdirectory, add a blank file called to each subdirectory (with two underscores on each side).

Now to import the module and use a function called foo() do:

from NewProject import MyModule as m

That is, it's checking the main python directory you added to your PYTHONPATH, then looking within the NewProject subdirectory via the file for the module you're trying to import.

Brown Dwarf Synthetic Photometry

The goal here was to get the synthetic colors in the SDSS, 2MASS and WISE filters of ~2000 model objects generated by the PHOENIX stellar and planetary atmosphere software.

Since it would be silly (and incredibly slow... and much more boring) to just calculate and store every single color for all 12 filter profiles, I wrote a module to calculate colors a la carte.

The Filters

I got the J, H, and K band relative spectral response (RSR) curves in the 2MASS documentation, the u, g, r, i and z bands from the SDSS documentation, and the W1, W2, W3, and W4 bands from the WISE documentation.

I dumped all my .txt filter files into one directory and wrote a function to grab them all, pull out the wavelength and transmission values, and output the filter name in position [0], x-values in [1], and y-values in [2]:

def get_filters(filter_directory):
  import glob, os
  files = glob.glob(filter_directory+'*.txt')
  if len(files) == 0:
    print 'No filters in', filter_directory
    filter_names = [os.path.splitext(os.path.basename(i))[0] for i in files]
    RSR = [open(i) for i in files]
    filt_data = [filter(None,[map(float,i.split()) for i in j if not i.startswith('#')]) for j in RSR]
    for i in RSR: i.close()
    RSR_x = [[x[0] for x in i] for i in filt_data]
    RSR_y = [[y[1] for y in i] for i in filt_data]
    filters = {}
    for i,j,k in zip(filter_names,RSR_x,RSR_y):
      filters[i] = j, k, center(i)
    return filters

Calculating Apparent Magnitudes

We can't have colors without magnitudes so here's a function to grab the Teff and log g specified spectra, and calculate the apparent magnitudes in a particular band:

def mags(band, teff='', logg='', bin=1):
  from import readsav
  from collections import Counter
  from scipy import trapz, log10, interp
  s = readsav(path+'')
  Fr, Wr = [i for i in s.modelspec['fsyn']], [i for i in s['wsyn']]
  Tr, Gr = [int(i) for i in s.modelspec['teff']], [round(i,1) for i in s.modelspec['logg']]
  # The band to compute
  RSR_x, RSR_y, lambda_eff = get_filters(path)[band]
  # Option to specify an effective temperature value
  if teff:
    t = [i for i, x in enumerate(s.modelspec['teff']) if x == teff]
    if len(t) == 0:
      print "No such effective temperature! Please choose from 1400K to 4500K in 50K increments or leave blank to select all."
    t = range(len(s.modelspec['teff']))
  # Option to specify a surfave gravity value
  if logg:
    g = [i for i, x in enumerate(s.modelspec['logg']) if x == logg]
    if len(g) == 0:
      print "No such surface gravity! Please choose from 3.0 to 6.0 in 0.1 increments or leave blank to select all."
    g = range(len(s.modelspec['logg']))
  # Pulls out objects that fit criteria above
  obj = list((Counter(t) & Counter(g)).elements())
  F = [Fr[i][::bin] for i in obj]
  T = [Tr[i] for i in obj]
  G = [Gr[i] for i in obj]
  W = Wr[::bin]
  # Interpolate to find new filter y-values
  I = interp(W,RSR_x,RSR_y,left=0,right=0)
  # Convolve the interpolated flux with each filter (FxR = RxF)
  FxR = [convolution(i,I) for i in F]
  # Integral of RSR curve over all lambda
  R0 = trapz(I,x=W)
  # Integrate to find the spectral flux density per unit wavelength [ergs][s-1][cm-2] then divide by R0 to get [erg][s-1][cm-2][cm-1]
  F_lambda = [trapz(y,x=W)/R0 for y in FxR]
  # Calculate apparent magnitude of each spectrum in each filter band
  Mags = [round(-2.5*log10(m/F_lambda_0(band)),3) for m in F_lambda]
  result = sorted(zip(Mags, T, G, F, I, FxR), key=itemgetter(1,2))
  return result

Calculating Colors

Now we can calculate the colors. Next, I wrote a function to accept any two bands with options to specify a surface gravity and/or effective temperature as well as a bin size to cut down on computation. Here's the code:

def colors(first, second, teff='', logg='', bin=1):
  (Mags_a, T, G) = [[i[j] for i in get_mags(first, teff=teff, logg=logg, bin=bin)[1:]] for j in range(3)]
  Mags_b = [i[0] for i in get_mags(second, teff=teff, logg=logg, bin=bin)[1:]]
  colors = [round(a-b,3) for a,b in zip(Mags_a,Mags_b)]
  print_mags(first, colors, T, G, second=second)
  return [colors, T, G]

The PHOENIX code gives the flux as Fλ in cgs units [erg][s-1][cm-2][cm-1] but as long as both spectra are in the same units the colors will be the same.

Makin' It Handsome

Then I wrote a short function to print out the magnitudes or colors in the Terminal:

def print_mags(first, Mags, T, G, second=''):
  LAYOUT = "{!s:10} {!s:10} {!s:25}"
  if second:
    print LAYOUT.format("Teff", "log g", first+'-'+second)
    print LAYOUT.format("Teff", "log g", first)
  for i,j,k in sorted(zip(T, G, Mags)):
    print LAYOUT.format(i, j, k)

The Output

Then if I just want the J-K color for objects with log g = 4.0 over the entire range of effective temperatures, I launch ipython and just do:

In [1]: import syn_phot as s
In [2]: s.colors('J','K', logg=4)
Teff -------- log g -------- J-K
1400.0 ------ 4.0 ---------- 4.386
1450.0 ------ 4.0 ---------- 4.154
4450.0 ------ 4.0 ---------- 0.756
4500.0 ------ 4.0 ---------- 0.733

Similarly, I can specify just the target effective temperature and get the whole range of surface gravities. Or I can specify an effective temperature AND a specific gravity to get the color of just that one object with:

In [3]: s.colors('i','W2', teff=3050, logg=5)
Teff -------- log g -------- J-K
3050.0 ------ 5.0 ---------- 3.442

I can also reduce the number of data points in each flux array if my sample is very large. I just have to specify the number of data points to skip with the "bin" optional parameter. For example:

In [4]: s.colors('W1','W2', teff=1850, bin=3)

This will calculate the W1-W2 color for all the objects with Teff = 1850K and all gravities, but only take every third flux value.

I also wrote functions to generate color-color, color-parameter and color-magnitude plots but those will be in a different post.


Here are a few color-parameter animated plots I made using my code. Here's how I made them. Click to animate!

And here are a few colorful-colorful color-color plots I made:

Plots with observational data

Just to be sure I'm on base, here's a color-color plot of J-H vs. H-Ks for objects with a log surface gravity of 5 dex (blue dots) plotted over some data for the Chamaeleon I Molecular Cloud (semi-transparent) from Carpenter et al. (2002).

The color scale is for main sequence stars and the black dots are probable members of the group. Cooler dwarfs move up and to the right.

And here's a plot of J-Ks vs. z-Ks as well as J-Ks vs. z-J. Again, the blue dots are from my synthetic photometry code at log(g)=5 and the semi-transparent points with errors are from Dahn et al. (2002).