Difference between revisions of "Python"
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=== Thermo-Physical Properties of Materials === | === Thermo-Physical Properties of Materials === | ||
| − | Have a look at [http://coolprop.org CoolProp] | + | Have a look at [http://coolprop.org CoolProp]. It is a thermo-physical property database with properties of many common pure substances. It has a python package as well as an Excel add-in. |
=== Symbolic computing === | === Symbolic computing === | ||
The [http://www.sympy.org sympy module] is a very capable symbolic module for Python. It plays well with the [http://ipython.org/notebook.html IPython notebook] | The [http://www.sympy.org sympy module] is a very capable symbolic module for Python. It plays well with the [http://ipython.org/notebook.html IPython notebook] | ||
Revision as of 12:14, 8 July 2016
Contents
Python
Python is a popular scripting language. It is on the top 10 of the TIOBE index, and is often used in scientific programming outside of the major commercial platforms like Matlab or Mathematica.
- Check out the Python website for more information
Installation
- Windows: The MPR module uses Python(x,y). This is a distribution which supplies a full scientific programming environment. If that local link does not work, look for a Python(x,y) executable installer here or download directly from the Python(x,y) website. Note that the Anaconda distribution is probably a lot better. There is a local mirror of that here. Use Anaconda3.
- Linux: Install python along with Matplotlib and Numpy/Scipy
- Mac: Use Anaconda.
Python 2 vs Python 3
Python 3 came out in 2008. The problem was that it was not backwards compatible with all Python 2 programs. This delayed adoption, especially in the scientific community. There is probably no reason to use Python 2 any more.
Scientific computing
Numeric calculations are done using the NumPy or SciPy modules. Here is a handy starting point for someone used to Matlab/Octave to get into NumpPy/Scipy.
Plotting is done using the matplotlib library. The website contains documentation as well as a large gallery of examples.
The SciPy website also has a lot of examples in their Cookbook. Topical ones include
Thermo-Physical Properties of Materials
Have a look at CoolProp. It is a thermo-physical property database with properties of many common pure substances. It has a python package as well as an Excel add-in.
Symbolic computing
The sympy module is a very capable symbolic module for Python. It plays well with the IPython notebook
