By Ivan Idris
If you’re a Python developer with uncomplicated NumPy talents, the 70+ recipes during this awesome cookbook will enhance your talents very quickly. learn how to elevate productiveness degrees and code speedier and purifier with the open resource mathematical library.
Today's global of technological know-how and know-how is all approximately pace and suppleness. by way of clinical computing, NumPy is at the most sensible of the record. NumPy provides you with either pace and excessive productivity.
"NumPy Cookbook" will train you all approximately NumPy, a number one medical computing library. NumPy replaces most of the performance of Matlab and Mathematica, yet unlike these items, it's loose and open source.
"Numpy Cookbook" will train you to put in writing readable, effective, and speedy code that's as as regards to the language of arithmetic up to attainable with the innovative open resource NumPy software program library.
You will know about fitting and utilizing NumPy and similar strategies. on the finish of the ebook, we are going to discover comparable clinical computing projects.
This e-book provide you with a fantastic beginning in NumPy arrays and common features. additionally, you will know about plotting with Matplotlib and the similar SciPy venture via examples.
"NumPy Cookbook" can assist you to be efficient with NumPy and write fresh and quick code.
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Example text
We will download an audio file and make a new version that is quieter. How to do it... Let's start by reading a WAV file: 1. Reading a WAV file. We will use a standard Python code to download an audio file of Austin Powers called "Smashing, baby". SciPy has a wavfile module, which allows you to load sound data or generate WAV files. If SciPy is installed, then we should have this module already. The read function returns a data array and sample rate. read(WAV_FILE) 2. Plot the original WAV data.
This should be easy for you, if you followed the boolean indexing piece in the previous chapter: eventerms = fib[fib % 2 == 0] print eventerms There we go: [ 2 196418 8 34 144 610 2584 10946 46368 832040 3524578] For completeness, following is the complete code for this recipe: import numpy #Each new term in the Fibonacci sequence is generated by adding the previous two terms. #By starting with 1 and 2, the first 10 terms will be: #1, 2, 3, 5, 8, 13, 21, 34, 55, 89, ... #By considering the terms in the Fibonacci sequence whose values do not exceed four million, #find the sum of the even-valued terms.
Make sure that everything, including all the optional packages is installed. You probably already have a Fortran compiler installed for NumPy. com/tools/. ff Installing SciPy using easy_install or pip: Install with either of the following two commands: sudo pip install scipy easy_install scipy ff Installing on Windows: If you have Python installed already, the preferred method is to download and use the binary distribution. Alternatively, you may want to install the Enthought Python distribution, which comes with other scientific Python software packages.