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Scientific Programming

Speed Testing

  • Numpy is about the same speed as Matlab (both 50X faster than just Python), and about a 10X speedup is gained by going to some form of C++ (good examples in the document)


  • Matlab looks great for analyzing files quickly, code is quite efficient character-wise for prototyping, but not really for long-term usage apparently.
    • Costs a lot of money ($5000+) once you get to industry and is a lot of headache to deal with license server and stuff


Another great option is the scientific toolboxes in Python.

  • See top link for great Python tutorial
  • Python(x,y) is the best to start with. Includes most packages you need
    • In Python 3.0, integer division will return a float, e.g., 1/3 will be 0.3333… At Scipy 2006, Guido explicitly stated in his keynote talk that the design choice he made in Python (i.e., that n/m is floor(n/m)) was a mistake.
    • In Sage (, which is built on Python, we do some very minimal preparsing of input, so that 1/3 is the exact rational number 1/3 (instead of Python's stupid 1/3 == 0). We also replace, e.g., 2^3 by 23. Sage is does a lot of exact symbolic and high precision arithmetic, so 1/3 staying the rational 1/3 makes sense as the default (though one can easily change this).
  • Print out stack (useful for try / except stuff): traceback.print_exc()


# By Peter Norvig
ranks = ['--23456789TJQKA'.index(r) for r,c in hand] #outputs index of char...nice


list = []
for i in range(10):
numpy.concatenate(tuple(self.dict[key]), axis=0)

Fast File Importing

#Gather all the elements returning from the generator
big_list = list(the_generator)

The Verdict

I use matlab for offline analysis and python for on the fly processing <and GUIs, and end products>.
Matlab is more complete but heavier.
Python is open source and faster.

I use both, for different purposes.
programming/scientific.txt · Last modified: 2017/09/15 17:58 (external edit)