Archive for 2012

Updating Scisoft from 7.5 to 7.7 in Fedora 14

Posted November 23, 2012 By grigoris

Updating Scisoft in Fedora is rather easy (see the installation details). First if we have already a previous installation we can easily remove it by running (as root):

yum remove scisoft-\*

and check that the directory /scisoft has been totally removed (if something extra has been added the we should still see the scisoft directory with all the extras inside).
Then, we follow the same steps as the first time (for example see this installation notes). Since we are updating then the repository file should already exist. We don’t have anything more to do than just to edit the file /etc/yum.repos.d/scisoft.repo and edit the line containing the baseurl:


[scisoft]
name=Scisoft
baseurl=ftp://ftp.eso.org/scisoft/scisoft7.7/linux/fedora11/yum-repo
gpgcheck=0
enabled=1

Actually we just replace …7.5/… with …7.7/… . Then we install the scisoft normally:


yum clean all
yum install scisoft-\*

and everything should be just fine!

NOTE: during this installation only the scisoft-idllib-7.7.0-0.i386 was not installed due to the lack of scisoft-idl. And that’s … because it is not included (along with SuperMongo) as they are paid versions.
The installation can be skipped by using — skip-broken (after all, no need for IDL since we get Python 2.7.2 and Matplotlib 1.1.0!!!).

Linux backup and restore filesystem –notes

Posted November 23, 2012 By grigoris

A quick note on how to backup and restore (if necessary) the filesystem. We go to the root directory (/) and as root we run:

tar cvpzf backup.tgz --exclude=/lost+found --exclude=/backup.tgz /

c: create new archive
v: verbose
p: preserve permissions
z: gzip
f: use file (name of file=backup.tgz)

We exclude all the directories that we don’t want to back up (and especially the backup file itself!) and … run it!

In order to restore the system, we go again to the root directory and (as root) we run:

tar xvpfz backup.tgz -C /

WARNING: this will overwrite everything we have under your root directory (everything … means everything!).

-C: change to directory

After that, we re-create the excluded directories (like: mkdir lost+found) and we reboot our system!

[source]

matplotlib: missed errorbars in logarithmic plot

Posted November 22, 2012 By grigoris

While manipulating some data I came across to, what looked, a strange behavior with the errorbar of matplotlib, when plotting the results from linear to logarithmic scale.

Suppose these data:

import matplotlib.pyplot as plt
import numpy as np

s=[19.0, 20.0, 21.0, 22.0, 24.0]
v=[36.5, 66.814250000000001, 130.17750000000001, 498.57466666666664, 19.41]
verr=[0.28999999999999998, 80.075044597909169, 71.322124839818571, 650.11015891565125, 0.02]
plt.errorbar(s,v,yerr=verr)
plt.show()

and the result looked like this:

where the errorbars are obviously visible, but when I switched to the logarithmic scale, by using:

plt.yscale('log')
plt.show()

then the result was this:

where some errorbars are not visible (although their caps are still visible). So, what is going on?

I couldn’t figure out what was the problem, so I asked at StackOverflow. I got an answer by “Dan”, in which he pointed to the fact that some of the errors were (that large) that led to negative values (not valid for log). That’s why they were not displayed. The solution was to use asymmetric errors, by substituting basically the negative error values with a value close to 0 (actually the plotted value, but slightly over 0).
The piece of code that corrected the display is:

import matplotlib.pyplot as plt
import numpy as np

s=[19.0, 20.0, 21.0, 22.0, 24.0]
v=np.array([36.5, 66.814250000000001, 130.17750000000001, 498.57466666666664, 19.41])
verr=np.array([0.28999999999999998, 80.075044597909169, 71.322124839818571, 650.11015891565125, 0.02])
verr2 = np.array(verr)
verr2[np.where(v-verr<=.0)] = v[np.where(v-verr<=.0)]*.999999 plt.errorbar(s,v,yerr=[verr2,verr]) plt.ylim(1E1,1E4) plt.yscale('log') plt.show()

with the final output (by adjusting also the axes):

Neptune and Triton from Skinakas

Posted November 4, 2012 By grigoris

Adding full paths – the THELI case

Posted October 30, 2012 By grigoris

In order to install THELI (GUI version) there is a series of libs and compilers to have already installed prior to THELI (like the fast-fourier transform (fftw), TIFF libraries (libtiff), C and C++ compilers (gcc, g++), Qt3, see the list at the site). Although everything was installed there was still an error in the installation process arising from the possible lack of qmake. The related command in the install.sh script was:

qmake -o Makefile theli.pro

The theli.pro existed, qmake was also present in the computer, so … what was wrong? Finally, Giannis Kapetanakis from the computer support pointed to the use of the full path of qmake. So the previous line should change to:

/usr/lib/qt-3.3/bin/qmake -o Makefile theli.pro

and everything worked properly!

Lesson learned: add the full paths of programs when everything seems to be in place but still they do not work!

Pluto from Skinakas

Posted October 5, 2012 By grigoris

Planetary imaging from Skinakas telescope

Posted October 5, 2012 By grigoris

In late August 2012 we (me and Manos Kardasis) tested the video capture method for planetary imaging using the 1.29m Skinakas‘ telescope. Although not aware of what problems to expect we finally didn’t encounter any (as Manos had been really working on this with great care and caution) but for the weather and seeing.
So, the test proved successful and all that is needed next time it good seeing.
Below you can see images of the equipment used and the results on Jupiter, Uranus and Neptune.
(Jupiter observations were also forwarded to Planetary Virtual Observatory & Laboratory / Jupiter section.)

Skinakas' 1.29m equipped for planetary imaging

 

Equipment: DMK/DBK video camera + barlow 2x (if needed) + filter wheel (L,R,G,B,Ch4,IR,UV) + eyepiece with flip mirror

 

Blank Fields

Posted October 4, 2012 By grigoris

Stumbled upon a very interesting page by Santos Pedraz Marcos with a collection of standard stars, I found this table of blank fields:

 
Name     RA1950     DE1950      Reference 
Roeser1  00 16 25.0 +16 09 00   CL 0016+16
Cadis1H  01 45 01.3 +02 05 07   Cadis
Blank1   04 25 46.0 +54 09 03   PASP 97,363
Cadis9H  09 10 28.0 +46 26 23   Cadis
Cadis10H 10 49 00.0 +57 41 52   Cadis, Lockman Hole
Cadis10H 10 49 00.0 +57 51 52   Cadis
Blank2   12 26 49.8 +02 17 56   PASP 97,363
BlankUH  13 07 47.9 +36 15 08   priv.com. 3 brighter *, else empty
HDF      13 34 35.5 +62 29 28   Williams et al 1996 AJ 112,1335
Cadis13H 13 45 11.4 +05 52 32   Cadis
Cadis13H 13 45 51.4 +05 52 32   Cadis
Cadis16H 16 23 28.9 +55 50 47   Cadis
Blank3   16 49 42.0 -15 21 00   PASP 97,363 (? RA:1982,DE:1982)
Cadis18H 17 59 43.0 +66 21 20   Cadis (NEP)
Blank4   19 19 09.0 +12 22 05   PASP 97,363
Blank5   21 26 54.4 -08 51 41   PASP 97,363
Cadis23H 23 13 16.5 +11 10 09   Cadis
Roeser2  23 45 45.0 +00 40 40   priv.com.
Blank6   23 54 08.9 +59 28 18   PASP 97,363

Transformation factors for X-ray fluxes

Posted October 4, 2012 By grigoris

In the following tables are given the transformation factors of the fluxes from a specific X-ray energy band to the 0.5-7 band, assuming Gamma 1.7 with Nh 6e20 and 0.

Table 1 >>>

Gamma    Nh   MinE MaxE   ModeledFx  Fx_in_0.5-7_band
1.7   6.0E20  2.0  25.0   7.4353E-9  1.524470492137473
1.7   6.0E20  0.1  2.0    2.177E-9   0.44635349756468984
1.7   6.0E20  3.0  10.0   3.234E-9   0.6630717726446831
1.7   6.0E20  2.0  30.0   8.2332E-9  1.6880650974147609
1.7   6.0E20  2.0  10.0   4.0686E-9  0.8341910736421079
1.7   6.0E20  0.3  10.0   6.2155E-9  1.2743731397684144
1.7   6.0E20  0.7  10.0   5.6647E-9  1.1614417444752492
1.7   6.0E20  0.2  10.0   6.245E-9   1.2804215595974016
1.7   6.0E20  0.2  12.0   6.836E-9   1.401595187981426
1.7   6.0E20  0.5  7.0    4.8773E-9  0.9999999789407894
1.7   6.0E20  0.1  10.0   6.2456E-9  1.2805445712067978
1.7   6.0E20  0.2  0.5    2.8144E-10 0.05770405966125076
1.7   6.0E20  0.5  1.0    7.3497E-10 0.1506919758411837
1.7   6.0E20  1.0  2.5    1.6072E-9  0.32952659047456384
1.7   6.0E20  0.5  2.0    1.895E-9   0.3885346266886183
1.7   6.0E20  1.0  2.0    1.16E-9    0.23783650481957802
1.7   6.0E20  2.0  4.5    1.7909E-9  0.36719086048976335
1.7   6.0E20  4.5  12.0   2.8688E-9  0.5881943055328245
1.7   6.0E20  2.5  7.0    2.5352E-9  0.5197957988828394
1.7   6.0E20  0.5  10.0   5.9636E-9  1.222725654804594
1.7   6.0E20  2.5  10.0   3.6215E-9  0.742521474746644

 

Table 2 >>>

Gamma Nh   MinE MaxE  ModeledFx  Fx_in_0.5-7_band
1.7   0.0  2.0  25.0  7.4574E-9  1.4222179900505934
1.7   0.0  0.1  2.0   3.9046E-9  0.7446553253977446
1.7   0.0  3.0  10.0  3.2422E-9  0.618327465238828
1.7   0.0  2.0  30.0  8.2554E-9  1.5744063925281622
1.7   0.0  2.0  10.0  4.0902E-9  0.7800515263759988
1.7   0.0  0.3  10.0  6.9496E-9  1.3253742741638779
1.7   0.0  0.7  10.0  5.8724E-9  1.119938952929684
1.7   0.0  0.2  10.0  7.3779E-9  1.407056368582311
1.7   0.0  0.2  12.0  7.9692E-9  1.519824622996969
1.7   0.0  0.5  7.0   5.2435E-9  0.999999972639108
1.7   0.0  0.1  10.0  7.9948E-9  1.5247068517737434
1.7   0.0  0.2  0.5   1.0472E-9  0.1997139302730967
1.7   0.0  0.5  1.0   1.0005E-9  0.19080766971838653
1.7   0.0  1.0  2.5   1.696E-9   0.32344807992770214
1.7   0.0  0.5  2.0   2.2406E-9  0.42730998909437085
1.7   0.0  1.0  2.0   1.2401     2.3650234120924503E8
1.7   0.0  2.0  4.5   1.8094E-9  0.34507484756230744
1.7   0.0  4.5  12.0  2.872E-9   0.547725750200595
1.7   0.0  2.5  7.0   2.547E-9   0.4857442441663391
1.7   0.0  0.5  10.0  6.3307E-9  1.2073424171358944
1.7   0.0  2.5  10.0  3.6342E-9  0.6930866886631255 

 

[Tables calculated through XSPECv12 by Vallia Antoniou]

 

Cleaning the 3.5m mirror of NTT-ESO

Posted August 30, 2012 By grigoris

That’s an impressive image of how to clean the main, 3.54m diameter, mirror of the New Technology Telescope (NTT) on La Silla. “This very delicate process is performed using a natural sponge, soft soap and distilled water”, as stated at ESO’s site.

 

Cleaning the 3.5m mirror of NTT-ESO (Credit:ESO)