Here are some references on C++:
C++ FAQ
Don’t be Afraid of Returning by Value, Know the Return Value Optimization
Copy elision
You will want to use the python Pandas (check here for Pandas tips) for analyzing your data. It is in particular very well suited for time series analysis and enable you to very easily manipulate dates, missing data, etc.
Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics. Check here for seaborn tips
Check out this page to find many links on machine learning.
You should install Spacepy on your machine. Then import the module spacepy.pycdf to load CDF files. Documentation for PYCDF is here
We will need to download and load some SPICE kernels (that indicate the position of the satellite, its orientation ...etc) from the NAIF servers.
The SPICE kernels file contents are summarized below:
S- Spacecraft ephemeris, given as a function of time. (SPK)
P- Planet, satellite, comet, or asteroid ephemerides, or more generally, location of any target body, given as a function of time. (also SPK)
The P kernel also logically includes certain physical, dynamical and cartographic constants for target bodies, such as size and shape specifications, and orientation of the spin axis and prime meridian. (PCK)
I- Instrument description kernel, containing descriptive data peculiar to a particular scientific instrument, such as field-of-view size, shape and orientation parameters. (IK)
C- Pointing kernel, containing a transformation, traditionally called the "C-matrix," which provides time-tagged pointing (orientation) angles for a spacecraft bus or a spacecraft structure upon which science instruments are mounted. A C-kernel may also include angular rate data for that structure. (CK)
E- Events kernel, summarizing mission activities - both planned and unanticipated. Events data are contained in the SPICE EK file set, which consists of three components: Science Plans, Sequences, and Notes. (EK)
Some additional data products are also important components of the SPICE system, even if not contained in the "SPICE" acronym.
A "frames kernel" (FK) contains specifications for the assortment of reference frames that are typically used by flight projects. This file also includes mounting alignment information for instruments, antennas and perhaps other structures of interest.
Spacecraft clock (SCLK) and leap seconds (LSK) kernels are also part of SPICE; these are used in converting time tags between various time measurement systems.
Under development is a digital shape model kernel (DSK) for both small, irregularly shaped bodies such as asteroids and comet nuclei, and for large, more uniformly shaped bodies such as the moon, earth and Mars. Other kernel types can be added as requirements arise and time permits.
For more information, please consult the NAIF Homepage
and
Spiceypy for PYTHON
We should visit the CASSINI Spacecraft Attitude tool.
We choose the "Time Range", "Time Interval", and SSE Spacecraft Axes (SSE) for the "Attitude Type".
Copy constructor for class having abstract class as attribute
Secrets of good OO design
check the notebook gallery : Links to the best IPython and Jupyter Notebooks.
Use google sanitizers, here is a video that explains what they are and here is the github access.
You can have very good information on CPU caches on this blog page.
There is a very good and complete review on computer memories here. In particular the section 6 is good to know for performance programming.
Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
Usually with Python you may ear from your colleagues "Hey, you may use package X to do this it's much better than Y and easier" then you discover that package X isn't packaged in your distribution or you install it and it mess up your system. For example Jupyter isn't packaged yet in Fedora and installing it directly with pip may break your already installed version of IPython-3x.
To use last version of your favourite Python tools such as Jupyter notebooks or Pandas on your system without messing up your computer, virtualenv is a solution.
It will allow you to install any python package with pip in an isolated environment so your system will not see your packages until you activate it.
You will be able to have as many virtual environments as you want with different packages and different versions in each environment.
On Fedora:
sudo dnf install python*-virtualenv python-qt5-devel python3-qt5-devel
On Mac OS with port:
sudo port install py35-virtualenv
sudo port install py27-virtualenv
Will install both Python2 and Python3 versions of virtualenv.
sudo mkdir /opt/Py2Venv /opt/Py3Venv
sudo chown -R <yourLogin> /opt/Py2Venv /opt/Py3Venv
virtualenv-2.7 --system-site-packages /opt/Py2Venv
virtualenv-3.4 --system-site-packages /opt/Py3Venv
Now you have two basic virtual environments for Python 2 and 3. Note that you can remove the --system-site-packages flag to tell that you want your environment to ignore system-wide packages it may protect you from some local and global packages incompatibilities. In most cases you may create it with --system-site-packages flag.
Your environment is ready to play, to use it you have to activate it.
source /opt/Py3Venv/bin/activate
Then it will override your system pip command by your current virtualenv one and all packages installed with pip while your environment is activated will be installed in your environment.
If the activate command worked you may see you shell prompt like this:
(Py3Venv)[adminlpp@pc-instru opt]$
Note the (Py3Venv) this says that Py3Venv is activated. To quit/deactivate it just use the command deactivate.
Let's install Pandas, Jupyter, numpy...
(Py3Venv)[adminlpp@pc-instru opt]$ pip install pandas
(Py3Venv)[adminlpp@pc-instru opt]$ pip install jupyter
Note that some python packages depends on system libraries, you may need to install them plus the devel packages
S : Separation of concerns principle
O : Open-Closed principle
L : Liskov substitution Principle
I : Interface segregation Principle
D : Dependency Inversion Principle
A Random Walk Through Geek-Space
I Like Big Bits
Log Structured Merge Trees
Algebraic patterns - Semigroup
EXACT STRING MATCHING ALGORITHMS
A Memory Allocator
LL and LR Parsing Demystified
Falsehoods programmers believe about time
GCC optimizations for embedded software
Useful libraries for data science in Python · GitHub
Example Google Style Python Docstrings
Welcome to Bokeh
Notebook Gallery
A gallery of interesting IPython Notebooks
SciPy 2016: "Data Science is Software" tutorial
Bit Twiddling Hacks
The Lost Art of C Structure Packing
Memory management in C programs
Const and Optimization in C
You Can't Always Hash Pointers in C
Four Ways to Compile C for Windows
You will find here material to help you get started with your coding. Depending on whether you mostly do data analysis or numerical modeling, you'll need slightly different tools and methods. the advice we give you will help to be efficient, rigorous and to write code than you'll be able to use, maintain and share in the long run. Now remember, If you're a PhD or a post-doc, following these advice will not only help you improving the quality and reproducibility of your science, but also will make all your coding efforts reusable for your future you and by people in the lab once you're gone. More importantly, it will give you the basic knowledge you need to legitimately claim for a data science / computing science position in the private sector.
Now, what are you doing?
Code Review for Teams Too Busy to Review Code (youtube video)
Nice tutorial on code review with Rhodecode
Videos
Optimizations
These are useful links to check out regularly
PRACE training: https://events.prace-ri.eu/category/2/
Catalogue of courses: http://formation-calcul.fr/
Formation IDRIS: https://cours.idris.fr/php-plan/affiche_planning.php?total
1 - show me your snippet
2 - explique à ton voisin
3 - montre nous ton blog préféré
4 - pull me quizz
5 - sell me (some features of ) your editor
6 - critic my code
7 - show me a youtube video
8 - optimize my snippet
9 - translate my code
10 - Ze lib of Ze Week