Reading the book of F.D. Seward and P.A. Charles, “Exploring the X-ray Universe”, I found out some really massive and luminous O type stars that are close by and can be pointed out easily at the night sky:
| star | distance (kpc) | magnitude (V) | spectral type |
|---|---|---|---|
| ζ Pup | 0.44 | 2.3 | O4If |
| δ Ori | 0.5 | 2.2 | O9.5II |
| θ Ori C (*) | 0.5 | 5.1 | O6V |
| ζ Ori | 0.5 | 1.6 | O9.7Ib |
| η Car (*) | 2.6 | -1 to 7 | LBV |
(*): binary systems
iPython is a great tool to work with Python interactively, imitating a MatLab environment especially when running with matplotlib and numpy [the pylab module, all can be automatically imported by starting pylab like: ipython –pylab]. So you can use it to load data and start playing around with them. Although I like to script more these actions it can be really fast and easy to use it when you just want to see some data and do some minor tasks. But since I don’t write them down these actions I keep forgetting them (well… after a serious number of repetitions this would be of no need … but up to that point let me keep a note!).
So, one of these is hot to make multiple plots. After creating (or most probably loading) the data and making all the necessary steps then we follow this typing:
In [23]: fig = figure()
In [24]: ax = fig.add_subplot(111)
In [25]: p1 = ax.plot(x,y,'r-')
In [26]: p2 = ax.plot(x,z,'g-')
In [27]: draw()
which gives us the following result:
Stumbled upon the various different licenses I though to write down some conclusions (if I really got into any…).
A nice comparison table of free software licenses is given in the Wikipedia where it is easily to see which licenses allow: (i) the link with other licenses and (ii) releasing under different license. So, in this table it is obvious that the GNU General Public License does not allow any change or link in order to preserve the license through the works, while the GNU Lesser General Public License allows only the link with other licenses. But that’s not the same with the permissive licenses like BSD or MIT. That means that these licenses (including the Python’s license) allow both link and distribution with other licenses, which makes them not compatible with copyleft licenses (with the best representative the GNU GPL).
Moreover, IRAF‘s license (Wikipedia / IRAF) is provided free of charge but under various licenses (MIT and some non-free parts). So its license is not really clarified and certainly not compatible with GNU GPL. Although Python provides the opportunity to connect with other licenses and redistribute code under GNU GPL the use of IRAF’s tasks does not allowed it as a whole package. So, the license for code that combines these packages cannot implemented under a copyleft license (like GNU CPL). It seems that a more suitable license would be a Modified BSD (like PyRAF’s license).
[edited on Dec 16th:] Actually since Python and MIT’s license of IRAF (& PyRAF) allow license changes, then scripts that use these tools can have whatever license the author wants, since Python and IRAF/PyRAF are distributed separately.
Thanks to Jann K for clarifying a little more what I have written before!
While running a data reduction of IR observations with IRAF, the ccdproc printed this error:
ERROR (502, “floating point invalid operation”)
which means that some values of the image used (the flat image in this case) are not suitable for the performing mathematical actions (like division with 0s). Moreover the IR ccds have many bad pixels so the best is to replace these values with standard ones in order to know exactly which areas are problematic.
In order to replace them the imcalc command (stsdas / toolbox / imgtools) can be used to replace these values, like:
imcalc master_flat_temp.fits output="master_flat.fits" equals="if (im1.le.0.0) then -9.0 else im1"
if we want for example to replace all values lower or equal (le) to 0.0 with -9.0.
Flatcombine and Darkcombine (based on imcombine both) in IRAF just combine the images given as input to create a master flat / dark image without performing any normalization. This is done when using ccdproc with the parameters flatcor and darkcor set to yes.
Although hedit of IRAF is supposed to handle the keywords of a fits file (add, remove) its behaviour can be misleading sometimes. An error like this:
ERROR (1121, “FXF: EOF encountered while reading FITS file (something.fits)”)
means that after altering the keywords in the header the EOF of the header has been placed wrongly. The solution is to place some dummy keywords more… but it seems to be an insisting bug.
The routine loadtxt from numpy (numpy.loadtxt) can be used to load columns of data from various files. This works great as long as numbers exist and becomes buggy with strings.
Suppose we have a file (named ‘test.test’) with these values:
3103725. 1616.93596535 13.656 0 2011-05-23T23:49:35 3139474. 1405.95436047 13.643 0 2011-05-23T23:51:16 3026925. 1370.07921223 13.683 0 2011-05-23T23:54:40 ...
First, if we want to assign many values we have to use unpack parameter, like:
>>> x, y, z = loadtxt('test.test', unpack=True, usecols=(0,1,2))
and if one of these columns is strings then we should define this by using dtype:
>>> x, y, z = loadtxt('test.test', unpack=True, dtype=(float,float,'S19'), usecols=(0,1,4)) or
>>> x, y, z = loadtxt('test.test', unpack=True, dtype={'names':['n1','n2','s1'],'formats':['f','f','S19']}, usecols=(0,1,4))
and of course we should get 3 columns with the the third one to be a column of strings.
But … this will not happen! The unpack parameter is buggy up to 1.3.0 version (at least) and it has been corrected at a later version (it works after version 1.6 for sure). So since I didn’t have that version (and I didn’t want to spend time upgrading) a solution was given by Derek H. within AstroPy list. The unpack parameter is not used at all and the assignment is done by this workaround:
>>> a = loadtxt('test.test',dtype=[('x','f'),('y','f'),('z','S19')],usecols=[0,1,4])
x,y,z = a['x'], a['y'], a['z']
In order to replace a character in a string with Python replace command can be used easily: str.replace(old, new[, count])
[return a copy of the string with all occurrences of substring old replaced by new. If the optional argument count is given, only the first count occurrences are replaced.]
But if we want to change more than one character then we have to do it differently. A nice solution I found at Gomputor’s blog is by defining a function like this:
def replace_all(text, dic):
for i, j in dic.iteritems():
text = text.replace(i, j)
return textSo then we just have to give our string (text) and the list of changes (dic) like:st = '2011-05-23T22:31:35'
chgs = {'-':'','T':'.',':':''}
and use that function: >>>print st, replace_all(st,chgs)
2011-05-23T22:31:35 20110523.223135
Ubuntu 10.10 includes matplotlib-0.99 in their repositories, which is not the latest one, so you don’t get the latest cool features (like gridspec)! But as I need some of its features I decided to go on and install the latest version matplotlib-1.1.0, only 20 days after its launch !
First of all it cannot be found in any debian repositories and Ubuntu 11.10 includes the 1.0.1 version (though I found some debian packages but it is either in “testing” mode or unstable). So the only solution seemed to build it from scratch …
After some search a reliable post was found and by following the commands matplotlib-1.1.0 was successfully built and installed. After downloading the latest version for matplotlib, untar it into a directory (it can be safely deleted afterwards) and follow these commands:
In order to build you need to install:
sudo apt-get install build-essential
and these dependencies:
sudo apt-get install python-dev python-scipy libfreetype6-dev libpng12-dev zlib1g-dev
[the last 3 libraries “libgtk2.0-dev python-gtk2-dev python-wxgtk2.8” found in the original post are not really needed, the libgtk2.0-dev & python-gtk2-dev were not included and python-wxgtk2.8 was already installed]
As it is stated in the post by Tim Teatro
” To see what we’re getting and why, read on.
To finalize the procedure:
python setup.py build
sudo python setup.py install
The installation of the new matplotlib version can be found at /usr/local/lib/python2.6/dist-packages/ . If there is no error when running matplotlib but still you cannot see any plot then probably the backend must change. Go to the matplotlibrc file and edit the backend:
cd /usr/local/lib/python2.6/dist-packages/matplotlib/mpl-data/
sudo gedit matplotlibrc &
and find the keyword backend and change it accordingly. The value that worked for me was WXAgg.
Useful stuff: commands for matplotlib / running a script with keyword ––verbose-helpful (check what is printed)
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