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Nim is a general-purpose, multi-paradigm, statically typed, compiled high-level systems programming language, designed and developed by a team around Andreas Rumpf. Nim is designed to be "efficient, expressive, and elegant", supporting metaprogramming, functional, message passing, procedural, and object-oriented programming styles by providing several features such as compile time code generation, algebraic data types, a foreign function interface (FFI) with C, C++, Objective-C, and JavaScript, and supporting compiling to those same languages as intermediate representations. Nim was created to be a language as fast as C, as expressive as Python, and as extensible as Lisp. Nim is statically typed. It supports compile-time metaprogramming features such as syntactic macros and term rewriting macros. Term rewriting macros enable library implementations of common data structures, such as bignums and matrices, to be implemented efficiently and with syntactic integration, as if they were built-in language facilities. Iterators are supported and can be used as first class entities, as can functions, allowing for the use of functional programming methods. Object-oriented programming is supported by inheritance and multiple dispatch. Functions can be generic and overloaded, and generics are further enhanced by Nim's support for type classes. Operator overloading is also supported. Nim includes multiple tunable memory management strategies, including tracing garbage collection, reference counting, and fully manual systems, with the default being deterministic reference counting with optimizations via move semantics and cycle collection via trial deletion. [Nim] ... presents a most original design that straddles Pascal and Python and compiles to C code or JavaScript. Nim compiles to C, C++, JavaScript, Objective-C, and LLVM. Nim's initial development was started in 2005 by Andreas Rumpf. It was originally named Nimrod when the project was made public in 2008. The first version of the Nim compiler was written in Pascal using the Free Pascal compiler.
[2]Paolo Prandoni, [3]LCAV, EPFL
This notebook shows a simple way to process audio files (in WAV format)
in a block-by-block fashion; this is useful in two cases
1. when you want to process a data stream in real time but you can
accept a small processing delay; in this case a buffered approach
can yield computational gains
2. when you need to process a large file that does not fit entirely
into [2]Paolo Prandoni, [3]LCAV, EPFL
In this notebook we will build a dual-tone multi-frequency decoder
based on the short-time Fourier transform.
In [1]:
[2]Paolo Prandoni, [3]LCAV, EPFL
The Karplus-Strong algorithm is a simple method to generate synthetic
sounds with arbitrary pitch. In this notebook we will explore a few
variations of the method to illustrate some basic notions of
discrete-time signal processing.
In [1]:
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import IPython
In [2]:
plt.rcParams["figure.figsize"] [2]Paolo Prandoni, [3]LCAV, EPFL
The Parks-McClellan (PM) algorithm is a clever application of advanced
polynomial fitting techniques to the problem of FIR filter design. In
this notebook we will explore the key ideas behind the method by
considering the design of a simple Type-I lowpass filter.
NOTE: This notebook uses interactive widgets. If the interactive
controls do not work or show up, please fo